Resolving ambiguity in a statement

ABSTRACT

A method includes generating a plurality of entigen groups from a set of phrases of a statement and identifying two plausible entigen groups based on a true meaning interpretation of the statement. The method further includes identifying a related entigen group based on a phrase of the statement and interpreting each of the two plausible entigen groups in light of the related entigen group to determine whether one of the two plausible entigen groups is a more likely interpretation of the statement than the other one of the two plausible entigen groups. When the one of the two plausible entigen groups is the more likely interpretation of the statement, the method further includes updating the one of the two plausible entigen groups using the related entigen group to produce an updated entigen group and adding the statement as the updated entigen group to a knowledge database.

CROSS REFERENCE TO RELATED PATENTS

The present U.S. Utility patent application claims priority pursuant to35 U.S.C. § 119(e) to U.S. Provisional Application No. 62/648,354,entitled “VERIFYING CONTENT AUTHENTICITY WHEN EXTRACTING KNOWLEDGE,”filed Mar. 26, 2018, which is hereby incorporated herein by reference inits entirety and made part of the present U.S. Utility patentapplication for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

NOT APPLICABLE

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

NOT APPLICABLE

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computing systems and moreparticularly to generating data representations of data and analyzingthe data utilizing the data representations.

Description of Related Art

It is known that data is stored in information systems, such as filescontaining text. It is often difficult to produce useful informationfrom this stored data due to many factors. The factors include thevolume of available data, accuracy of the data, and variances in howtext is interpreted to express knowledge. For example, many languagesand regional dialects utilize the same or similar words to representdifferent concepts.

Computers are known to utilize pattern recognition techniques and applystatistical reasoning to process text to express an interpretation in anattempt to overcome ambiguities inherent in words. One patternrecognition technique includes matching a word pattern of a query to aword pattern of the stored data to find an explicit textual answer.Another pattern recognition technique classifies words into majorgrammatical types such as functional words, nouns, adjectives, verbs andadverbs. Grammar based techniques then utilize these grammatical typesto study how words should be distributed within a string of words toform a properly constructed grammatical sentence where each word isforced to support a grammatical operation without necessarilyidentifying what the word is actually trying to describe.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a computingsystem in accordance with the present invention;

FIG. 2 is a schematic block diagram of an embodiment of various serversof a computing system in accordance with the present invention;

FIG. 3 is a schematic block diagram of an embodiment of various devicesof a computing system in accordance with the present invention;

FIGS. 4A and 4B are schematic block diagrams of another embodiment of acomputing system in accordance with the present invention;

FIG. 4C is a logic diagram of an embodiment of a method for interpretingcontent to produce a response to a query within a computing system inaccordance with the present invention;

FIG. 5A is a schematic block diagram of an embodiment of a collectionsmodule of a computing system in accordance with the present invention;

FIG. 5B is a logic diagram of an embodiment of a method for obtainingcontent within a computing system in accordance with the presentinvention;

FIG. 5C is a schematic block diagram of an embodiment of a query moduleof a computing system in accordance with the present invention;

FIG. 5D is a logic diagram of an embodiment of a method for providing aresponse to a query within a computing system in accordance with thepresent invention;

FIG. 5E is a schematic block diagram of an embodiment of an identigenentigen intelligence (IEI) module of a computing system in accordancewith the present invention;

FIG. 5F is a logic diagram of an embodiment of a method for analyzingcontent within a computing system in accordance with the presentinvention;

FIG. 6A is a schematic block diagram of an embodiment of an elementidentification module and an interpretation module of a computing systemin accordance with the present invention;

FIG. 6B is a logic diagram of an embodiment of a method for interpretinginformation within a computing system in accordance with the presentinvention;

FIG. 6C is a schematic block diagram of an embodiment of an answerresolution module of a computing system in accordance with the presentinvention;

FIG. 6D is a logic diagram of an embodiment of a method for producing ananswer within a computing system in accordance with the presentinvention;

FIG. 7A is an information flow diagram for interpreting informationwithin a computing system in accordance with the present invention;

FIG. 7B is a relationship block diagram illustrating an embodiment ofrelationships between things and representations of things within acomputing system in accordance with the present invention;

FIG. 7C is a diagram of an embodiment of a synonym words table within acomputing system in accordance with the present invention;

FIG. 7D is a diagram of an embodiment of a polysemous words table withina computing system in accordance with the present invention;

FIG. 7E is a diagram of an embodiment of transforming words intogroupings within a computing system in accordance with the presentinvention;

FIG. 8A is a data flow diagram for accumulating knowledge within acomputing system in accordance with the present invention;

FIG. 8B is a diagram of an embodiment of a groupings table within acomputing system in accordance with the present invention;

FIG. 8C is a data flow diagram for answering questions utilizingaccumulated knowledge within a computing system in accordance with thepresent invention;

FIG. 8D is a data flow diagram for answering questions utilizinginterference within a computing system in accordance with the presentinvention;

FIG. 8E is a relationship block diagram illustrating another embodimentof relationships between things and representations of things within acomputing system in accordance with the present invention;

FIGS. 8F and 8G are schematic block diagrams of another embodiment of acomputing system in accordance with the present invention;

FIG. 8H is a logic diagram of an embodiment of a method for processingcontent to produce knowledge within a computing system in accordancewith the present invention;

FIGS. 8J and 8K are schematic block diagrams another embodiment of acomputing system in accordance with the present invention;

FIG. 8L is a logic diagram of an embodiment of a method for generating aquery response to a query within a computing system in accordance withthe present invention;

FIG. 9A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 9B is a schematic block diagram of an embodiment of a contentblockchain in accordance with the present invention;

FIG. 9C is a logic diagram of an embodiment of a method for verifyingauthenticity of content that is utilized to create knowledge within acomputing system in accordance with the present invention;

FIG. 10A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIGS. 10B-10C are data flow diagrams of embodiments of a method toprocess ambiguity within a computing system in accordance with thepresent invention;

FIG. 10D is a logic diagram of an embodiment of a method for processingambiguity within a computing system in accordance with the presentinvention;

FIG. 11A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 11B is a logic diagram of an embodiment of a method for optimizinga knowledge base within a computing system in accordance with thepresent invention;

FIG. 12A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 12B is a data flow diagram for providing an answer to a questionwith regards to likelihood of a crime within a computing system inaccordance with the present invention;

FIG. 12C is a logic diagram of an embodiment of a method for providingan answer to a question with regards to likelihood of a crime within acomputing system in accordance with the present invention;

FIG. 13A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 13B is a data flow diagram for providing an answer to a questionwith regards to likelihood of a sequence trigger detection within acomputing system in accordance with the present invention;

FIG. 13C is a logic diagram of an embodiment of a method for providingan answer to a question with regards to likelihood of a sequence triggerdetection within a computing system in accordance with the presentinvention;

FIG. 14A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 14B is a logic diagram of an embodiment of a method for providingan answer to a question with regards to optimizing user interfaceparameters within a computing system in accordance with the presentinvention;

FIG. 15A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 15B is a data flow diagram for an example of operation of a firstembodiment of identifying an undesired document artifact within thecomputing system in accordance with the present invention;

FIGS. 15C-15E are further data flow diagrams for examples of operationof a second embodiment of identifying the undesired document artifactwithin the computing system.

FIG. 15F is a logic diagram of an embodiment of a method identifying anundesired document artifact within a computing system in accordance withthe present invention;

FIG. 16A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 16B is a logic diagram of an embodiment of a method for utilizingmultiple database formats when creating knowledge from content within acomputing system in accordance with the present invention;

FIG. 17A is a schematic block diagram of another embodiment of a contentingestion module within a computing system in accordance with thepresent invention;

FIG. 17B is a logic diagram of an embodiment of a method for ingestingcontent when creating knowledge from content within a computing systemin accordance with the present invention;

FIG. 18A is a schematic block diagram of another embodiment of aninterpretation module within a computing system in accordance with thepresent invention; and

FIG. 18B is a logic diagram of an embodiment of a method forinterpreting content when creating knowledge from content within acomputing system in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a computingsystem 10 that includes a plurality of user devices 12-1 through 12-N, aplurality of wireless user devices 14-1 through 14-N, a plurality ofcontent sources 16-1 through 16-N, a plurality of transactional servers18-1 through 18-N, a plurality of artificial intelligence (AI) servers20-1 through 20-N, and a core network 24. The core network 24 includesat least one of the Internet, a public radio access network (RAN), andany private network. Hereafter, the computing system 10 may beinterchangeably referred to as a data network, a data communicationnetwork, a system, a communication system, and a data communicationsystem. Hereafter, the user device and the wireless user device may beinterchangeably referred to as user devices, and each of thetransactional servers and the AI servers may be interchangeably referredto as servers.

Each user device, wireless user device, transactional server, and AIserver includes a computing device that includes a computing core. Ingeneral, a computing device is any electronic device that cancommunicate data, process data, and/or store data. A further generalityof a computing device is that it includes one or more of a centralprocessing unit (CPU), a memory system, a sensor (e.g., internal orexternal), user input/output interfaces, peripheral device interfaces,communication elements, and an interconnecting bus structure.

As further specific examples, each of the computing devices may be aportable computing device and/or a fixed computing device. A portablecomputing device may be an embedded controller, a smart sensor, a smartpill, a social networking device, a gaming device, a cell phone, a smartphone, a robot, a personal digital assistant, a digital music player, adigital video player, a laptop computer, a handheld computer, a tablet,a video game controller, an engine controller, a vehicular controller,an aircraft controller, a maritime vessel controller, and/or any otherportable device that includes a computing core. A fixed computing devicemay be security camera, a sensor device, a household appliance, amachine, a robot, an embedded controller, a personal computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a cameracontroller, a video game console, a critical infrastructure controller,and/or any type of home or office computing equipment that includes acomputing core. An embodiment of the various servers is discussed ingreater detail with reference to FIG. 2. An embodiment of the variousdevices is discussed in greater detail with reference to FIG. 3.

Each of the content sources 16-1 through 16-N includes any source ofcontent, where the content includes one or more of data files, a datastream, a tech stream, a text file, an audio stream, an audio file, avideo stream, a video file, etc. Examples of the content sources includea weather service, a multi-language online dictionary, a fact server, abig data storage system, the Internet, social media systems, an emailserver, a news server, a schedule server, a traffic monitor, a securitycamera system, audio monitoring equipment, an information server, aservice provider, a data aggregator, and airline traffic server, ashipping and logistics server, a banking server, a financial transactionserver, etc. Alternatively, or in addition to, one or more of thevarious user devices may provide content. For example, a wireless userdevice may provide content (e.g., issued as a content message) when thewireless user device is able to capture data (e.g., text input, sensorinput, etc.).

Generally, an embodiment of this invention presents solutions where thecomputing system 10 supports the generation and utilization of knowledgeextracted from content. For example, the AI servers 20-1 through 20-Ningest content from the content sources 16-1 through 16-N by receiving,via the core network 24 content messages 28-1 through 28-N as AImessages 32-1 through 32-N, extract the knowledge from the ingestedcontent, and interact with the various user devices to utilize theextracted knowledge by facilitating the issuing, via the core network24, user messages 22-1 through 22-N to the user devices 12-1 through12-N and wireless signals 26-1 through 26-N to the wireless user devices14-1 through 14-N.

Each content message 28-1 through 28-N includes a content request (e.g.,requesting content related to a topic, content type, content timing, oneor more domains, etc.) or a content response, where the content responseincludes real-time or static content such as one or more of dictionaryinformation, facts, non-facts, weather information, sensor data, newsinformation, blog information, social media content, user daily activityschedules, traffic conditions, community event schedules, schoolschedules, user schedules airline records, shipping records, logisticsrecords, banking records, census information, global financial historyinformation, etc. Each AI message 32-1 through 32-N includes one or moreof content messages, user messages (e.g., a query request, a queryresponse that includes an answer to a query request), and transactionmessages (e.g., transaction information, requests and responses relatedto transactions). Each user message 22-1 through 22-N includes one ormore of a query request, a query response, a trigger request, a triggerresponse, a content collection, control information, softwareinformation, configuration information, security information, routinginformation, addressing information, presence information, analyticsinformation, protocol information, all types of media, sensor data,statistical data, user data, error messages, etc.

When utilizing a wireless signal capability of the core network 24, eachof the wireless user devices 14-1 through 14-N encodes/decodes dataand/or information messages (e.g., user messages such as user messages22-1 through 22-N) in accordance with one or more wireless standards forlocal wireless data signals (e.g., Wi-Fi, Bluetooth, ZigBee) and/or forwide area wireless data signals (e.g., 2G, 3G, 4G, 5G, satellite,point-to-point, etc.) to produce wireless signals 26-1 through 26-N.Having encoded/decoded the data and/or information messages, thewireless user devices 14-1 through 14-N and/receive the wireless signalsto/from the wireless capability of the core network 24.

As another example of the generation and utilization of knowledge, thetransactional servers 18-1 through 18-N communicate, via the corenetwork 24, transaction messages 30-1 through 30-N as further AImessages 32-1 through 32-N to facilitate ingesting of transactional typecontent (e.g., real-time crypto currency transaction information) and tofacilitate handling of utilization of the knowledge by one or more ofthe transactional servers (e.g., for a transactional function) inaddition to the utilization of the knowledge by the various userdevices. Each transaction message 30-1 through 30-N includes one or moreof a query request, a query response, a trigger request, a triggerresponse, a content message, and transactional information, where thetransactional information may include one or more of consumer purchasinghistory, crypto currency ledgers, stock market trade information, otherinvestment transaction information, etc.

In another specific example of operation of the generation andutilization of knowledge extracted from the content, the user device12-1 issues a user message 22-1 to the AI server 20-1, where the usermessage 22-1 includes a query request and where the query requestincludes a question related to a first domain of knowledge. The issuingincludes generating the user message 22-1 based on the query request(e.g., the question), selecting the AI server 20-1 based on the firstdomain of knowledge, and sending, via the core network 24, the usermessage 22-1 as a further AI message 32-1 to the AI server 20-1. Havingreceived the AI message 32-1, the AI server 20-1 analyzes the questionwithin the first domain, generates further knowledge, generates apreliminary answer, generates a quality level indicator of thepreliminary answer, and determines to gather further content when thequality level indicator is below a minimum quality threshold level.

When gathering the further content, the AI server 20-1 issues, via thecore network 24, a still further AI message 32-1 as a further contentmessage 28-1 to the content source 16-1, where the content message 28-1includes a content request for more content associated with the firstdomain of knowledge and in particular the question. Alternatively, or inaddition to, the AI server 20-1 issues the content request to another AIserver to facilitate a response within a domain associated with theother AI server. Further alternatively, or in addition to, the AI server20-1 issues the content request to one or more of the various userdevices to facilitate a response from a subject matter expert.

Having received the content message 28-1, the contents or 16-1 issues,via the core network 24, a still further content message 28-1 to the AIserver 20-1 as a yet further AI message 32-1, where the still furthercontent message 28-1 includes requested content. The AI server 20-1processes the received content to generate further knowledge. Havinggenerated the further knowledge, the AI server 20-1 re-analyzes thequestion, generates still further knowledge, generates anotherpreliminary answer, generates another quality level indicator of theother preliminary answer, and determines to issue a query response tothe user device 12-1 when the quality level indicator is above theminimum quality threshold level. When issuing the query response, the AIserver 20-1 generates an AI message 32-1 that includes another usermessage 22-1, where the other user message 22-1 includes the otherpreliminary answer as a query response including the answer to thequestion. Having generated the AI message 32-1, the AI server 20-1sends, via the core network 24, the AI message 32-1 as the user message22-1 to the user device 12-1 thus providing the answer to the originalquestion of the query request.

FIG. 2 is a schematic block diagram of an embodiment of the AI servers20-1 through 20-N and the transactional servers 18-1 through 18-N of thecomputing system 10 of FIG. 1. The servers include a computing core 52,one or more visual output devices 74 (e.g., video graphics display,touchscreen, LED, etc.), one or more user input devices 76 (e.g.,keypad, keyboard, touchscreen, voice to text, a push button, amicrophone, a card reader, a door position switch, a biometric inputdevice, etc.), one or more audio output devices 78 (e.g., speaker(s),headphone jack, a motor, etc.), and one or more visual input devices 80(e.g., a still image camera, a video camera, photocell, etc.).

The servers further include one or more universal serial bus (USB)devices (USB devices 1-U), one or more peripheral devices (e.g.,peripheral devices 1-P), one or more memory devices (e.g., one or moreflash memory devices 92, one or more hard drive (HD) memories 94, andone or more solid state (SS) memory devices 96, and/or cloud memory 98).The servers further include one or more wireless location modems 84(e.g., global positioning satellite (GPS), Wi-Fi, angle of arrival, timedifference of arrival, signal strength, dedicated wireless location,etc.), one or more wireless communication modems 86-1 through 86-N(e.g., a cellular network transceiver, a wireless data networktransceiver, a Wi-Fi transceiver, a Bluetooth transceiver, a 315 MHztransceiver, a zig bee transceiver, a 60 GHz transceiver, etc.), a telcointerface 102 (e.g., to interface to a public switched telephonenetwork), and a wired local area network (LAN) 88 (e.g., optical,electrical), and a wired wide area network (WAN) 90 (e.g., optical,electrical).

The computing core 52 includes a video graphics module 54, one or moreprocessing modules 50-1 through 50-N (e.g., which may include one ormore secure co-processors), a memory controller 56 and one or more mainmemories 58-1 through 58-N (e.g., RAM serving as local memory). Thecomputing core 52 further includes one or more input/output (I/O) deviceinterfaces 62, an input/output (I/O) controller 60, a peripheralinterface 64, one or more USB interfaces 66, one or more networkinterfaces 72, one or more memory interfaces 70, and/or one or moreperipheral device interfaces 68.

The processing modules may be a single processing device or a pluralityof processing devices where the processing device may further bereferred to as one or more of a “processing circuit”, a “processor”,and/or a “processing unit”. Such a processing device may be amicroprocessor, micro-controller, digital signal processor,microcomputer, central processing unit, field programmable gate array,programmable logic device, state machine, logic circuitry, analogcircuitry, digital circuitry, and/or any device that manipulates signals(analog and/or digital) based on hard coding of the circuitry and/oroperational instructions.

The processing module, module, processing circuit, and/or processingunit may be, or further include, memory and/or an integrated memoryelement, which may be a single memory device, a plurality of memorydevices, and/or embedded circuitry of another processing module, module,processing circuit, and/or processing unit. Such a memory device may bea read-only memory, random access memory, volatile memory, non-volatilememory, static memory, dynamic memory, flash memory, cache memory,and/or any device that stores digital information. Note that if theprocessing module, module, processing circuit, and/or processing unitincludes more than one processing device, the processing devices may becentrally located (e.g., directly coupled together via a wired and/orwireless bus structure) or may be distributedly located (e.g., cloudcomputing via indirect coupling via a local area network and/or a widearea network).

Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

Each of the interfaces 62, 66, 68, 70, and 72 includes a combination ofhardware (e.g., connectors, wiring, etc.) and may further includeoperational instructions stored on memory (e.g., driver software) thatare executed by one or more of the processing modules 50-1 through 50-Nand/or a processing circuit within the interface. Each of the interfacescouples to one or more components of the servers. For example, one ofthe IO device interfaces 62 couples to an audio output device 78. Asanother example, one of the memory interfaces 70 couples to flash memory92 and another one of the memory interfaces 70 couples to cloud memory98 (e.g., an on-line storage system and/or on-line backup system). Inother embodiments, the servers may include more or less devices andmodules than shown in this example embodiment of the servers.

FIG. 3 is a schematic block diagram of an embodiment of the variousdevices of the computing system 10 of FIG. 1, including the user devices12-1 through 12-N and the wireless user devices 14-1 through 14-N. Thevarious devices include the visual output device 74 of FIG. 2, the userinput device 76 of FIG. 2, the audio output device 78 of FIG. 2, thevisual input device 80 of FIG. 2, and one or more sensors 82.

The sensor may be implemented internally and/or externally to thedevice. Example sensors includes a still camera, a video camera, servomotors associated with a camera, a position detector, a smoke detector,a gas detector, a motion sensor, an accelerometer, velocity detector, acompass, a gyro, a temperature sensor, a pressure sensor, an altitudesensor, a humidity detector, a moisture detector, an imaging sensor, anda biometric sensor. Further examples of the sensor include an infraredsensor, an audio sensor, an ultrasonic sensor, a proximity detector, amagnetic field detector, a biomaterial detector, a radiation detector, aweight detector, a density detector, a chemical analysis detector, afluid flow volume sensor, a DNA reader, a wind speed sensor, a winddirection sensor, and an object detection sensor.

Further examples of the sensor include an object identifier sensor, amotion recognition detector, a battery level detector, a roomtemperature sensor, a sound detector, a smoke detector, an intrusiondetector, a motion detector, a door position sensor, a window positionsensor, and a sunlight detector. Still further sensor examples includemedical category sensors including: a pulse rate monitor, a heart rhythmmonitor, a breathing detector, a blood pressure monitor, a blood glucoselevel detector, blood type, an electrocardiogram sensor, a body massdetector, an imaging sensor, a microphone, body temperature, etc.

The various devices further include the computing core 52 of FIG. 2, theone or more universal serial bus (USB) devices (USB devices 1-U) of FIG.2, the one or more peripheral devices (e.g., peripheral devices 1-P) ofFIG. 2, and the one or more memories of FIG. 2 (e.g., flash memories 92,HD memories 94, SS memories 96, and/or cloud memories 98). The variousdevices further include the one or more wireless location modems 84 ofFIG. 2, the one or more wireless communication modems 86-1 through 86-Nof FIG. 2, the telco interface 102 of FIG. 2, the wired local areanetwork (LAN) 88 of FIG. 2, and the wired wide area network (WAN) 90 ofFIG. 2. In other embodiments, the various devices may include more orless internal devices and modules than shown in this example embodimentof the various devices.

FIGS. 4A and 4B are schematic block diagrams of another embodiment of acomputing system that includes one or more of the user device 12-1 ofFIG. 1, the wireless user device 14-1 of FIG. 1, the content source 16-1of FIG. 1, the transactional server 18-1 of FIG. 1, the user device 12-2of FIG. 1, and the AI server 20-1 of FIG. 1. The AI server 20-1 includesthe processing module 50-1 (e.g., associated with the servers) of FIG.2, where the processing module 50-1 includes a collections module 120,an identigen entigen intelligence (IEI) module 122, and a query module124. Alternatively, the collections module 120, the IEI module 122, andthe query module 124 may be implemented by the processing module 50-1(e.g., associated with the various user devices) of FIG. 3. Thecomputing system functions to interpret content to produce a response toa query.

FIG. 4A illustrates an example of the interpreting of the content toproduce the response to the query where the collections module 120interprets (e.g., based on an interpretation approach such as rules) atleast one of a collections request 132 from the query module 124 and acollections request within collections information 130 from the IEImodule 122 to produce content request information (e.g., potentialsources, content descriptors of desired content). Alternatively, or inaddition to, the collections module 120 may facilitate gathering furthercontent based on a plurality of collection requests from a plurality ofdevices of the computing system 10 of FIG. 1.

The collections request 132 is utilized to facilitate collection ofcontent, where the content may be received in a real-time fashion onceor at desired intervals, or in a static fashion from previous discretetime frames. For instance, the query module 124 issues the collectionsrequest 132 to facilitate collection of content as a background activityto support a long-term query (e.g., how many domestic airline flightsover the next seven days include travelers between the age of 18 and 35years old). The collections request 132 may include one or more of arequester identifier (ID), a content type (e.g., language, dialect,media type, topic, etc.), a content source indicator, securitycredentials (e.g., an authorization level, a password, a user ID,parameters utilized for encryption, etc.), a desired content qualitylevel, trigger information (e.g., parameters under which to collectcontent based on a pre-event, an event (i.e., content quality levelreaches a threshold to cause the trigger, trueness), or a timeframe), adesired format, and a desired timing associated with the content.

Having interpreted the collections request 132, the collections module120 selects a source of content based on the content requestinformation. The selecting includes one or more of identifying one ormore potential sources based on the content request information,selecting the source of content from the potential sources utilizing aselection approach (e.g., favorable history, a favorable security level,favorable accessibility, favorable cost, favorable performance, etc.).For example, the collections module 120 selects the content source 16-1when the content source 16-1 is known to provide a favorable contentquality level for a domain associated with the collections request 132.

Having selected the source of content, the collections module 120 issuesa content request 126 to the selected source of content. The issuingincludes generating the content request 126 based on the content requestinformation for the selected source of content and sending the contentrequest 126 to the selected source of content. The content request 126may include one or more of a content type indicator, a requester ID,security credentials for content access, and any other informationassociated with the collections request 132. For example, thecollections module 120 sends the content request 126, via the corenetwork 24 of FIG. 1, to the content source 16-1. Alternatively, or inaddition to, the collections module 120 may send a similar contentrequest 126 to one or more of the user device 12-1, the wireless userdevice 14-1, and the transactional server 18-1 to facilitate collectingof further content.

In response to the content request 126, the collections module 120receives one or more content responses 128. The content response 128includes one or more of content associated with the content source, acontent source identifier, security credential processing information,and any other information pertaining to the desired content. Havingreceived the content response 128, the collections module 120 interpretsthe received content response 128 to produce collections information130, where the collections information 130 further includes acollections response from the collections module 120 to the IEI module122.

The collections response includes one or more of transformed content(e.g., completed sentences and paragraphs), timing informationassociated with the content, a content source ID, and a content qualitylevel. Having generated the collections response of the collectionsinformation 130, the collections module 120 sends the collectionsinformation 130 to the IEI module 122. Having received the collectionsinformation 130 from the collections module 120, the IEI module 122interprets the further content of the content response to generatefurther knowledge, where the further knowledge is stored in a memoryassociated with the IEI module 122 to facilitate subsequent answering ofquestions posed in received queries.

FIG. 4B further illustrates the example of the interpreting of thecontent to produce the response to the query where, the query module 124interprets a received query request 136 from a requester to produce aninterpretation of the query request. For example, the query module 124receives the query request 136 from the user device 12-2, and/or fromone or more of the wireless user device 14-2 and the transactionalserver 18-2. The query request 136 includes one or more of an identifier(ID) associated with the request (e.g., requester ID, ID of an entity tosend a response to), a question, question constraints (e.g., within atimeframe, within a geographic area, within a domain of knowledge,etc.), and content associated with the question (e.g., which may beanalyzed for new knowledge itself).

The interpreting of the query request 136 includes determining whetherto issue a request to the IEI module 122 (e.g., a question, perhaps withcontent) and/or to issue a request to the collections module 120 (e.g.,for further background content). For example, the query module 124produces the interpretation of the query request to indicate to send therequest directly to the IEI module 122 when the question is associatedwith a simple non-time varying function answer (e.g., question: “howmany hydrogen atoms does a molecule of water have?”).

Having interpreted the query request 136, the query module 124 issues atleast one of an IEI request as query information 138 to the IEI module122 (e.g., when receiving a simple new query request) and a collectionsrequest 132 to the collections module 120 (e.g., based on two or morequery requests 136 requiring more substantive content gathering). TheIEI request of the query information 138 includes one or more of anidentifier (ID) of the query module 124, an ID of the requester (e.g.,the user device 12-2), a question (e.g., with regards to content foranalysis, with regards to knowledge minded by the AI server from generalcontent), one or more constraints (e.g., assumptions, restrictions,etc.) associated with the question, content for analysis of thequestion, and timing information (e.g., a date range for relevance ofthe question).

Having received the query information 138 that includes the IEI requestfrom the query module 124, the IEI module 122 determines whether asatisfactory response can be generated based on currently availableknowledge, including that of the query request 136. The determiningincludes indicating that the satisfactory response cannot be generatedwhen an estimated quality level of an answer falls below a minimumquality threshold level. When the satisfactory response cannot begenerated, the IEI module 122 facilitates collecting more content. Thefacilitating includes issuing a collections request to the collectionsmodule 120 of the AI server 20-1 and/or to another server or userdevice, and interpreting a subsequent collections response 134 ofcollections information 130 that includes further content to producefurther knowledge to enable a more favorable answer.

When the IEI module 122 indicates that the satisfactory response can begenerated, the IEI module 122 issues an IEI response as queryinformation 138 to the query module 124. The IEI response includes oneor more of one or more answers, timing relevance of the one or moreanswers, an estimated quality level of each answer, and one or moreassumptions associated with the answer. The issuing includes generatingthe IEI response based on the collections response 134 of thecollections information 130 and the IEI request, and sending the IEIresponse as the query information 138 to the query module 124.Alternatively, or in addition to, at least some of the further contentcollected by the collections module 120 is utilized to generate acollections response 134 issued by the collections module 120 to thequery module 124. The collections response 134 includes one or more offurther content, a content availability indicator (e.g., when, where,required credentials, etc.), a content freshness indicator (e.g.,timestamps, predicted time availability), content source identifiers,and a content quality level.

Having received the query information 138 from the IEI module 122, thequery module 124 issues a query response 140 to the requester based onthe IEI response and/or the collections response 134 directly from thecollections module 120, where the collection module 120 generates thecollections response 134 based on collected content and the collectionsrequest 132. The query response 140 includes one or more of an answer,answer timing, an answer quality level, and answer assumptions.

FIG. 4C is a logic diagram of an embodiment of a method for interpretingcontent to produce a response to a query within a computing system. Inparticular, a method is presented for use in conjunction with one ormore functions and features described in conjunction with FIGS. 1-3,4A-4B, and also FIG. 4C. The method includes step 150 where acollections module of a processing module of one or more computingdevices (e.g., of one or more servers) interprets a collections requestto produce content request information. The interpreting may include oneor more of identifying a desired content source, identifying a contenttype, identifying a content domain, and identifying content timingrequirements.

The method continues at step 152 where the collections module selects asource of content based on the content request information. For example,the collections module identifies one or more potential sources based onthe content request information and selects the source of content fromthe potential sources utilizing a selection approach (e.g., based on oneor more of favorable history, a favorable security level, favorableaccessibility, favorable cost, favorable performance, etc.). The methodcontinues at step 154 where the collections module issues a contentrequest to the selected source of content. The issuing includesgenerating a content request based on the content request informationfor the selected source of content and sending the content request tothe selected source of content.

The method continues at step 156 where the collections module issuescollections information to an identigen entigen intelligence (IEI)module based on a received content response, where the IEI moduleextracts further knowledge from newly obtained content from the one ormore received content responses. For example, the collections modulegenerates the collections information based on newly obtained contentfrom the one or more received content responses of the selected sourceof content.

The method continues at step 158 where a query module interprets areceived query request from a requester to produce an interpretation ofthe query request. The interpreting may include determining whether toissue a request to the IEI module (e.g., a question) or to issue arequest to the collections module to gather further background content.The method continues at step 160 where the query module issues a furthercollections request. For example, when receiving a new query request,the query module generates a request for the IEI module. As anotherexample, when receiving a plurality of query requests for similarquestions, the query module generates a request for the collectionsmodule to gather further background content.

The method continues at step 162 where the IEI module determines whethera satisfactory query response can be generated when receiving therequest from the query module. For example, the IEI module indicatesthat the satisfactory query response cannot be generated when anestimated quality level of an answer is below a minimum answer qualitythreshold level. The method branches to step 166 when the IEI moduledetermines that the satisfactory query response can be generated. Themethod continues to step 164 when the IEI module determines that thesatisfactory query response cannot be generated. When the satisfactoryquery response cannot be generated, the method continues at step 164where the IEI module facilitates collecting more content. The methodloops back to step 150.

When the satisfactory query response can be generated, the methodcontinues at step 166 where the IEI module issues an IEI response to thequery module. The issuing includes generating the IEI response based onthe collections response and the IEI request, and sending the IEIresponse to the query module. The method continues at step 168 where thequery module issues a query response to the requester. For example, thequery module generates the query response based on the IEI responseand/or a collections response from the collections module and sends thequery response to the requester, where the collections module generatesthe collections response based on collected content and the collectionsrequest.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 5A is a schematic block diagram of an embodiment of the collectionsmodule 120 of FIG. 4A that includes a content acquisition module 180, acontent selection module 182, a source selection module 184, a contentsecurity module 186, an acquisition timing module 188, a contenttransformation module 190, and a content quality module 192. Generally,an embodiment of this invention presents solutions where the collectionsmodule 120 supports collecting content.

In an example of operation of the collecting of the content, the contentacquisition module 180 receives a collections request 132 from arequester. The content acquisition module 180 obtains content selectioninformation 194 based on the collections request 132. The contentselection information 194 includes one or more of content requirements,a desired content type indicator, a desired content source identifier, acontent type indicator, a candidate source identifier (ID), and acontent profile (e.g., a template of typical parameters of the content).For example, the content acquisition module 180 receives the contentselection information 194 from the content selection module 182, wherethe content selection module 182 generates the content selectioninformation 194 based on a content selection information request fromthe content acquisition module 180 and where the content acquisitionmodule 180 generates the content selection information request based onthe collections request 132.

The content acquisition module 180 obtains source selection information196 based on the collections request 132. The source selectioninformation 196 includes one or more of candidate source identifiers, acontent profile, selected sources, source priority levels, andrecommended source access timing. For example, the content acquisitionmodule 180 receives the source selection information 196 from the sourceselection module 184, where the source selection module 184 generatesthe source selection information 196 based on a source selectioninformation request from the content acquisition module 180 and wherethe content acquisition module 180 generates the source selectioninformation request based on the collections request 132.

The content acquisition module 180 obtains acquisition timinginformation 200 based on the collections request 132. The acquisitiontiming information 200 includes one or more of recommended source accesstiming, confirmed source access timing, source access testing results,estimated velocity of content update's, content precious, timestamps,predicted time availability, required content acquisition triggers,content acquisition trigger detection indicators, and a duplicativeindicator with a pending content request. For example, the contentacquisition module 180 receives the acquisition timing information 200from the acquisition timing module 188, where the acquisition timingmodule 188 generates the acquisition timing information 200 based on anacquisition timing information request from the content acquisitionmodule 180 and where the content acquisition module 180 generates theacquisition timing information request based on the collections request132.

Having obtained the content selection information 194, the sourceselection information 196, and the acquisition timing information 200,the content acquisition module 180 issues a content request 126 to acontent source utilizing security information 198 from the contentsecurity module 186, where the content acquisition module 180 generatesthe content request 126 in accordance with the content selectioninformation 194, the source selection information 196, and theacquisition timing information 200. The security information 198includes one or more of source priority requirements, requester securityinformation, available security procedures, and security credentials fortrust and/or encryption. For example, the content acquisition module 180generates the content request 126 to request a particular content typein accordance with the content selection information 194 and to includesecurity parameters of the security information 198, initiates sendingof the content request 126 in accordance with the acquisition timinginformation 200, and sends the content request 126 to a particulartargeted content source in accordance with the source selectioninformation 196.

In response to receiving a content response 128, the content acquisitionmodule 180 determines the quality level of received content extractedfrom the content response 128. For example, the content acquisitionmodule 180 receives content quality information 204 from the contentquality module 192, where the content quality module 192 generates thequality level of the received content based on receiving a contentquality request from the content acquisition module 180 and where thecontent acquisition module 180 generates the content quality requestbased on content extracted from the content response 128. The contentquality information includes one or more of a content reliabilitythreshold range, a content accuracy threshold range, a desired contentquality level, a predicted content quality level, and a predicted levelof trust.

When the quality level is below a minimum desired quality thresholdlevel, the content acquisition module 180 facilitates acquisition offurther content. The facilitating includes issuing another contentrequest 126 to a same content source and/or to another content source toreceive and interpret further received content. When the quality levelis above the minimum desired quality threshold level, the contentacquisition module 180 issues a collections response 134 to therequester. The issuing includes processing the content in accordancewith a transformation approach to produce transformed content,generating the collections response 134 to include the transformedcontent, and sending the collections response 134 to the requester. Theprocessing of the content to produce the transformed content includesreceiving content transformation information 202 from the contenttransformation module 190, where the content transformation module 190transforms the content in accordance with the transformation approach toproduce the transformed content. The content transformation informationincludes a desired format, available formats, recommended formatting,the received content, transformation instructions, and the transformedcontent.

FIG. 5B is a logic diagram of an embodiment of a method for obtainingcontent within a computing system. In particular, a method is presentedfor use in conjunction with one or more functions and features describedin conjunction with FIGS. 1-3, 4A-4C, 5A, and also FIG. 5B. The methodincludes step 210 where a processing module of one or more processingmodules of one or more computing devices of the computing systemreceives a collections request from the requester. The method continuesat step 212 where the processing module determines content selectioninformation. The determining includes interpreting the collectionsrequest to identify requirements of the content.

The method continues at step 214 where the processing module determinessource selection information. The determining includes interpreting thecollections request to identify and select one or more sources for thecontent to be collected. The method continues at step 216 where theprocessing module determines acquisition timing information. Thedetermining includes interpreting the collections request to identifytiming requirements for the acquisition of the content from the one ormore sources. The method continues at step 218 where the processingmodule issues a content request utilizing security information and inaccordance with one or more of the content selection information, thesource selection information, and the acquisition timing information.For example, the processing module issues the content request to the oneor more sources for the content in accordance with the contentrequirements, where the sending of the request is in accordance with theacquisition timing information.

The method continues at step 220 where the processing module determinesa content quality level for received content area the determiningincludes receiving the content from the one or more sources, obtainingcontent quality information for the received content based on a qualityanalysis of the received content. The method branches to step 224 whenthe content quality level is favorable and the method continues to step222 when the quality level is unfavorable. For example, the processingmodule determines that the content quality level is favorable when thecontent quality level is equal to or above a minimum quality thresholdlevel and determines that the content quality level is unfavorable whenthe content quality level is less than the minimum quality thresholdlevel.

When the content quality level is unfavorable, the method continues atstep 222 where the processing module facilitates acquisition and furthercontent. For example, the processing module issues further contentrequests and receives further content for analysis. When the contentquality level is favorable, the method continues at step 224 where theprocessing module issues a collections response to the requester. Theissuing includes generating the collections response and sending thecollections response to the requester. The generating of the collectionsresponse may include transforming the received content into transformedcontent in accordance with a transformation approach (e.g.,reformatting, interpreting absolute meaning and translating into anotherlanguage in accordance with the absolute meaning, etc.).

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 5C is a schematic block diagram of an embodiment of the querymodule 124 of FIG. 4A that includes an answer acquisition module 230, acontent requirements module 232 a source requirements module 234, acontent security module 236, an answer timing module 238, an answertransformation module 240, and an answer quality module 242. Generally,an embodiment of this invention presents solutions where the querymodule 124 supports responding to a query.

In an example of operation of the responding to the query, the answeracquisition module 230 receives a query request 136 from a requester.The answer acquisition module 230 obtains content requirementsinformation 248 based on the query request 136. The content requirementsinformation 248 includes one or more of content parameters, a desiredcontent type, a desired content source if any, a content type if any,candidate source identifiers, a content profile, and a question of thequery request 136. For example, the answer acquisition module 230receives the content requirements information 248 from the contentrequirements module 232, where the content requirements module 232generates the content requirements information 248 based on a contentrequirements information request from the answer acquisition module 230and where the answer acquisition module 230 generates the contentrequirements information request based on the query request 136.

The answer acquisition module 230 obtains source requirementsinformation 250 based on the query request 136. The source requirementsinformation 250 includes one or more of candidate source identifiers, acontent profile, a desired source parameter, recommended sourceparameters, source priority levels, and recommended source accesstiming. For example, the answer acquisition module 230 receives thesource requirements information 250 from the source requirements module234, where the source requirements module 234 generates the sourcerequirements information 250 based on a source requirements informationrequest from the answer acquisition module 230 and where the answeracquisition module 230 generates the source requirements informationrequest based on the query request 136.

The answer acquisition module 230 obtains answer timing information 254based on the query request 136. The answer timing information 254includes one or more of requested answer timing, confirmed answertiming, source access testing results, estimated velocity of contentupdates, content freshness, timestamps, predicted time available,requested content acquisition trigger, and a content acquisition triggerdetected indicator. For example, the answer acquisition module 230receives the answer timing information 254 from the answer timing module238, where the answer timing module 238 generates the answer timinginformation 254 based on an answer timing information request from theanswer acquisition module 230 and where the answer acquisition module230 generates the answer timing information request based on the queryrequest 136.

Having obtained the content requirements information 248, the sourcerequirements information 250, and the answer timing information 254, theanswer acquisition module 230 determines whether to issue an IEI request244 and/or a collections request 132 based on one or more of the contentrequirements information 248, the source requirements information 250,and the answer timing information 254. For example, the answeracquisition module 230 selects the IEI request 244 when an immediateanswer to a simple query request 136 is required and is expected to havea favorable quality level. As another example, the answer acquisitionmodule 230 selects the collections request 132 when a longer-term answeris required as indicated by the answer timing information to beforeand/or when the query request 136 has an unfavorable quality level.

When issuing the IEI request 244, the answer acquisition module 230generates the IEI request 244 in accordance with security information252 received from the content security module 236 and based on one ormore of the content requirements information 248, the sourcerequirements information 250, and the answer timing information 254.Having generated the IEI request 244, the answer acquisition module 230sends the IEI request 244 to at least one IEI module.

When issuing the collections request 132, the answer acquisition module230 generates the collections request 132 in accordance with thesecurity information 252 received from the content security module 236and based on one or more of the content requirements information 248,the source requirements information 250, and the answer timinginformation 254. Having generated the collections request 132, theanswer acquisition module 230 sends the collections request 132 to atleast one collections module. Alternatively, the answer acquisitionmodule 230 facilitate sending of the collections request 132 to one ormore various user devices (e.g., to access a subject matter expert).

The answer acquisition module 230 determines a quality level of areceived answer extracted from a collections response 134 and/or an IEIresponse 246. For example, the answer acquisition module 230 extractsthe quality level of the received answer from answer quality information258 received from the answer quality module 242 in response to an answerquality request from the answer acquisition module 230. When the qualitylevel is unfavorable, the answer acquisition module 230 facilitatesobtaining a further answer. The facilitation includes issuing at leastone of a further IEI request 244 and a further collections request 132to generate a further answer for further quality testing. When thequality level is favorable, the answer acquisition module 230 issues aquery response 140 to the requester. The issuing includes generating thequery response 140 based on answer transformation information 256received from the answer transformation module 240, where the answertransformation module 240 generates the answer transformationinformation 256 to include a transformed answer based on receiving theanswer from the answer acquisition module 230. The answer transformationinformation 250 6A further include the question, a desired format of theanswer, available formats, recommended formatting, received IEIresponses, transformation instructions, and transformed IEI responsesinto an answer.

FIG. 5D is a logic diagram of an embodiment of a method for providing aresponse to a query within a computing system. In particular, a methodis presented for use in conjunction with one or more functions andfeatures described in conjunction with FIGS. 1-3, 4A-4C, 5C, and alsoFIG. 5D. The method includes step 270 where a processing module of oneor more processing modules of one or more computing devices of thecomputing system receives a query request (e.g., a question) from arequester. The method continues at step 272 where the processing moduledetermines content requirements information. The determining includesinterpreting the query request to produce the content requirements. Themethod continues at step 274 where the processing module determinessource requirements information. The determining includes interpretingthe query request to produce the source requirements. The methodcontinues at step 276 where the processing module determines answertiming information. The determining includes interpreting the queryrequest to produce the answer timing information.

The method continues at step 278 the processing module determineswhether to issue an IEI request and/or a collections request. Forexample, the determining includes selecting the IEI request when theanswer timing information indicates that a simple one-time answer isappropriate. As another example, the processing module selects thecollections request when the answer timing information indicates thatthe answer is associated with a series of events over an event timeframe.

When issuing the IEI request, the method continues at step 280 where theprocessing module issues the IEI request to an IEI module. The issuingincludes generating the IEI request in accordance with securityinformation and based on one or more of the content requirementsinformation, the source requirements information, and the answer timinginformation.

When issuing the collections request, the method continues at step 282where the processing module issues the collections request to acollections module. The issuing includes generating the collectionsrequest in accordance with the security information and based on one ormore of the content requirements information, the source requirementsinformation, and the answer timing information. Alternatively, theprocessing module issues both the IEI request and the collectionsrequest when a satisfactory partial answer may be provided based on acorresponding IEI response and a further more generalized and specificanswer may be provided based on a corresponding collections response andassociated further IEI response.

The method continues at step 284 where the processing module determinesa quality level of a received answer. The determining includesextracting the answer from the collections response and/or the IEIresponse and interpreting the answer in accordance with one or more ofthe content requirements information, the source requirementsinformation, the answer timing information, and the query request toproduce the quality level. The method branches to step 288 when thequality level is favorable and the method continues to step 286 when thequality level is unfavorable. For example, the processing moduleindicates that the quality level is favorable when the quality level isequal to or greater than a minimum answer quality threshold level. Asanother example, the processing module indicates that the quality levelis unfavorable when the quality level is less than the minimum answerquality threshold level.

When the quality level is unfavorable, the method continues at step 286where the processing module obtains a further answer. The obtainingincludes at least one of issuing a further IEI request and a furthercollections request to facilitate obtaining of a further answer forfurther answer quality level testing as the method loops back to step270. When the quality level is favorable, the method continues at step288 where the processing module issues a query response to therequester. The issuing includes transforming the answer into atransformed answer in accordance with an answer transformation approach(e.g., formatting, further interpretations of the virtual question inlight of the answer and further knowledge) and sending the transformedanswer to the requester as the query response.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 5E is a schematic block diagram of an embodiment of the identigenentigen intelligence (IEI) module 122 of FIG. 4A that includes a contentingestion module 300, an element identification module 302, andinterpretation module 304, and answer resolution module 306, and an IEIcontrol module 308. Generally, an embodiment of this invention presentssolutions where the IEI module 122 supports interpreting content toproduce knowledge that may be utilized to answer questions.

In an example of operation of the producing and utilizing of theknowledge, the content ingestion module 300 generates formatted content314 based on question content 312 and/or source content 310, where theIEI module 122 receives an IEI request 244 that includes the questioncontent 312 and the IEI module 122 receives a collections response 134that includes the source content 310. The source content 310 includescontent from a source extracted from the collections response 134. Thequestion content 312 includes content extracted from the IEI request 244(e.g., content paired with a question). The content ingestion module 300generates the formatted content 314 in accordance with a formattingapproach (e.g., creating proper sentences from words of the content).The formatted content 314 includes modified content that is compatiblewith subsequent element identification (e.g., complete sentences,combinations of words and interpreted sounds and/or inflection cues withtemporal associations of words).

The element identification module 302 processes the formatted content314 based on element rules 318 and an element list 332 to produceidentified element information 340. Rules 316 includes the element rules318 (e.g., match, partial match, language translation, etc.). Lists 330includes the element list 332 (e.g., element ID, element context ID,element usage ID, words, characters, symbols etc.). The IEI controlmodule 308 may provide the rules 316 and the lists 330 by accessingstored data 360 from a memory associated with the IEI module 122.Generally, an embodiment of this invention presents solutions where thestored data 360 may further include one or more of a descriptivedictionary, categories, representations of element sets, element list,sequence data, pending questions, pending request, recognized elements,unrecognized elements, errors, etc.

The identified element information 340 includes one or more ofidentifiers of elements identified in the formatted content 314, mayinclude ordering and/or sequencing and grouping information. Forexample, the element identification module 302 compares elements of theformatted content 314 to known elements of the element list 332 toproduce identifiers of the known elements as the identified elementinformation 340 in accordance with the element rules 318. Alternatively,the element identification module 302 outputs un-identified elementinformation 342 to the IEI control module 308, where the un-identifiedelement information 342 includes temporary identifiers for elements notidentifiable from the formatted content 314 when compared to the elementlist 332.

The interpretation module 304 processes the identified elementinformation 340 in accordance with interpretation rules 320 (e.g.,potentially valid permutations of various combinations of identifiedelements), question information 346 (e.g., a question extracted from theIEI request to hundred 44 which may be paired with content associatedwith the question), and a groupings list 334 (e.g., representations ofassociated groups of representations of things, a set of elementidentifiers, valid element usage IDs in accordance with similar, anelement context, permutations of sets of identifiers for possibleinterpretations of a sentence or other) to produce interpretedinformation 344. The interpreted information 344 includes potentiallyvalid interpretations of combinations of identified elements. Generally,an embodiment of this invention presents solutions where theinterpretation module 304 supports producing the interpreted information344 by considering permutations of the identified element information340 in accordance with the interpretation rules 320 and the groupingslist 334.

The answer resolution module 306 processes the interpreted information344 based on answer rules 322 (e.g., guidance to extract a desiredanswer), the question information 346, and inferred question information352 (e.g., posed by the IEI control module or analysis of generalcollections of content or refinement of a stated question from arequest) to produce preliminary answers 354 and an answer quality level356. The answer generally lies in the interpreted information 344 asboth new content received and knowledge based on groupings list 334generated based on previously received content. The preliminary answers354 includes an answer to a stated or inferred question that subjectfurther refinement. The answer quality level 356 includes adetermination of a quality level of the preliminary answers 354 based onthe answer rules 322. The inferred question information 352 may furtherbe associated with time information 348, where the time informationincludes one or more of current real-time, a time reference associatedwith entity submitting a request, and a time reference of a collectionsresponse.

When the IEI control module 308 determines that the answer quality level356 is below an answer quality threshold level, the IEI control module308 facilitates collecting of further content (e.g., by issuing acollections request 132 and receiving corresponding collectionsresponses 134 for analysis). When the answer quality level 356 comparesfavorably to the answer quality threshold level, the IEI control module308 issues an IEI response 246 based on the preliminary answers 354.When receiving training information 358, the IEI control module 308facilitates updating of one or more of the lists 330 and the rules 316and stores the updated list 330 and the updated rules 316 in thememories as updated stored data 360.

FIG. 5F is a logic diagram of an embodiment of a method for analyzingcontent within a computing system. In particular, a method is presentedfor use in conjunction with one or more functions and features describedin conjunction with FIGS. 1-3, 4A-4C, 5E, and also FIG. 5F. The methodincludes step 370 where a processing module of one or more processingmodules of one or more computing devices of the computing systemfacilitates updating of one or more rules and lists based on one or moreof received training information and received content. For example, theprocessing module updates rules with received rules to produce updatedrules and updates element lists with received elements to produceupdated element lists. As another example, the processing moduleinterprets the received content to identify a new word for at leasttemporary inclusion in the updated element list.

The method continues at step 372 where the processing module transformsat least some of the received content into formatted content. Forexample, the processing module processes the received content inaccordance with a transformation approach to produce the formattedcontent, where the formatted content supports compatibility withsubsequent element identification (e.g., typical sentence structures ofgroups of words).

The method continues at step 374 where the processing module processesthe formatted content based on the rules and the lists to produceidentified element information and/or an identified element information.For example, the processing module compares the formatted content toelement lists to identify a match producing identifiers for identifiedelements or new identifiers for unidentified elements when there is nomatch.

The method continues at step 376 with a processing module processes theidentified element information based on rules, the lists, and questioninformation to produce interpreted information. For example, theprocessing module compares the identified element information toassociated groups of representations of things to generate potentiallyvalid interpretations of combinations of identified elements.

The method continues at step 378 where the processing module processesthe interpreted information based on the rules, the questioninformation, and inferred question information to produce preliminaryanswers. For example, the processing module matches the interpretedinformation to one or more answers (e.g., embedded knowledge based on afact base built from previously received content) with highestcorrectness likelihood levels that is subject to further refinement.

The method continues at step 380 where the processing module generatesan answer quality level based on the preliminary answers, the rules, andthe inferred question information. For example, the processing modulepredicts the answer correctness likelihood level based on the rules, theinferred question information, and the question information. The methodbranches to step 384 when the answer quality level is favorable and themethod continues to step 382 when the answer quality level isunfavorable. For example, the generating of the answer quality levelfurther includes the processing module indicating that the answerquality level is favorable when the answer quality level is greater thanor equal to a minimum answer quality threshold level. As anotherexample, the generating of the answer quality level further includes theprocessing module indicating that the answer quality level isunfavorable when the answer quality level is less than the minimumanswer quality threshold level.

When the answer quality level is unfavorable, the method continues atstep 382 where the processing module facilitates gathering clarifyinginformation. For example, the processing module issues a collectionsrequest to facilitate receiving further content and or request questionclarification from a question requester. When the answer quality levelis favorable, the method continues at step 384 where the processingmodule issues a response that includes one or more answers based on thepreliminary answers and/or further updated preliminary answers based ongathering further content. For example, the processing module generatesa response that includes one or more answers and the answer qualitylevel and issues the response to the requester. The method describedabove in conjunction with the processing module can alternatively beperformed by other modules of the computing system 10 of FIG. 1 or byother devices. In addition, at least one memory section (e.g., acomputer readable memory, a non-transitory computer readable storagemedium, a non-transitory computer readable memory organized into a firstmemory element, a second memory element, a third memory element, afourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 6A is a schematic block diagram of an embodiment of the elementidentification module 302 of FIG. 5A and the interpretation module 304of FIG. 5A. The element identification module 302 includes an elementmatching module 400 and an element grouping module 402. Theinterpretation module 304 includes a grouping matching module 404 and agrouping interpretation module 406. Generally, an embodiment of thisinvention presents solutions where the element identification module 302supports identifying potentially valid permutations of groupings ofelements while the interpretation module 304 interprets the potentiallyvalid permutations of groupings of elements to produce interpretedinformation that includes the most likely of groupings based on aquestion.

In an example of operation of the identifying of the potentially validpermutations of groupings of elements, when matching elements of theformatted content 314, the element matching module 400 generates matchedelements 412 (e.g., identifiers of elements contained in the formattedcontent 314) based on the element list 332. For example, the elementmatching module 400 matches a received element to an element of theelement list 332 and outputs the matched elements 412 to include anidentifier of the matched element. When finding elements that areunidentified, the element matching module 400 outputs un-recognizedwords information 408 (e.g., words not in the element list 332, maytemporarily add) as part of un-identified element information 342. Forexample, the element matching module 400 indicates that a match cannotbe made between a received element of the formatted content 314,generates the unrecognized words info 408 to include the receivedelement and/or a temporary identifier, and issues and updated elementlist 414 that includes the temporary identifier and the correspondingunidentified received element.

The element grouping module 402 analyzes the matched elements 412 inaccordance with element rules 318 to produce grouping error information410 (e.g., incorrect sentence structure indicators) when a structuralerror is detected. The element grouping module 402 produces identifiedelement information 340 when favorable structure is associated with thematched elements in accordance with the element rules 318. Theidentified element information 340 may further include groupinginformation of the plurality of permutations of groups of elements(e.g., several possible interpretations), where the grouping informationincludes one or more groups of words forming an associated set and/orsuper-group set of two or more subsets when subsets share a common coreelement.

In an example of operation of the interpreting of the potentially validpermutations of groupings of elements to produce the interpretedinformation, the grouping matching module 404 analyzes the identifiedelement information 340 in accordance with a groupings list 334 toproduce validated groupings information 416. For example, the groupingmatching module 404 compares a grouping aspect of the identified elementinformation 340 (e.g., for each permutation of groups of elements ofpossible interpretations), generates the validated groupings information416 to include identification of valid permutations aligned with thegroupings list 334. Alternatively, or in addition to, the groupingmatching module 404 generates an updated groupings list 418 whendetermining a new valid grouping (e.g., has favorable structure andinterpreted meaning) that is to be added to the groupings list 334.

The grouping interpretation module 406 interprets the validatedgroupings information 416 based on the question information 346 and inaccordance with the interpretation rules 320 to produce interpretedinformation 344 (e.g., most likely interpretations, next most likelyinterpretations, etc.). For example, the grouping interpretation module406 obtains context, obtains favorable historical interpretations,processes the validated groupings based on interpretation rules 320,where each interpretation is associated with a correctness likelihoodlevel.

FIG. 6B is a logic diagram of an embodiment of a method for interpretinginformation within a computing system. In particular, a method ispresented for use in conjunction with one or more functions and featuresdescribed in conjunction with FIGS. 1-3, 4A-4C, 5E-5F, 6A, and also FIG.6B. The method includes step 430 where a processing module of one ormore processing modules of one or more computing devices of thecomputing system analyzes formatted content. For example, the processingmodule attempt to match a received element of the formatted content toone or more elements of an elements list. When there is no match, themethod branches to step 434 and when there is a match, the methodcontinues to step 432. When there is a match, the method continues atstep 432 where the processing module outputs matched elements (e.g., toinclude the matched element and/or an identifier of the matchedelement). When there is no match, the method continues at step 434 wherethe processing module outputs unrecognized words (e.g., elements and/ora temporary identifier for the unmatched element).

The method continues at step 436 where the processing module analyzesmatched elements. For example, the processing module attempt to match adetected structure of the matched elements (e.g., chained elements as ina received sequence) to favorable structures in accordance with elementrules. The method branches to step 440 when the analysis is unfavorableand the method continues to step 438 when the analysis is favorable.When the analysis is favorable matching a detected structure to thefavorable structure of the element rules, the method continues at step438 where the processing module outputs identified element information(e.g., an identifier of the favorable structure, identifiers of each ofthe detected elements). When the analysis is unfavorable matching adetected structure to the favorable structure of the element rules, themethod continues at step 440 where the processing module outputsgrouping error information (e.g., a representation of the incorrectstructure, identifiers of the elements of the incorrect structure, atemporary new identifier of the incorrect structure).

The method continues at step 442 where the processing module analyzesthe identified element information to produce validated groupingsinformation. For example, the processing module compares a groupingaspect of the identified element information and generates the validatedgroupings information to include identification of valid permutationsthat align with the groupings list. Alternatively, or in addition to,the processing module generates an updated groupings list whendetermining a new valid grouping.

The method continues at step 444 where the processing module interpretsthe validated groupings information to produce interpreted information.For example, the processing module obtains one or more of context andhistorical interpretations and processes the validated groupings basedon interpretation rules to generate the interpreted information, whereeach interpretation is associated with a correctness likelihood level(e.g., a quality level).

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 6C is a schematic block diagram of an embodiment of the answerresolution module 306 of FIG. 5A that includes an interim answer module460, and answer prioritization module 462, and a preliminary answerquality module 464. Generally, an embodiment of this invention presentssolutions where the answer resolution module 306 supports producing ananswer for interpreted information 344.

In an example of operation of the providing of the answer, the interimanswer module 460 analyzes the interpreted information 344 based onquestion information 346 and inferred question information 352 toproduce interim answers 466 (e.g., answers to stated and/or inferredquestions without regard to rules that is subject to furtherrefinement). The answer prioritization module 462 analyzes the interimanswers 466 based on answer rules 322 to produce preliminary answer 354.For example, the answer prioritization module 462 identifies allpossible answers from the interim answers 466 that conform to the answerrules 322.

The preliminary answer quality module 464 analyzes the preliminaryanswers 354 in accordance with the question information 346, theinferred question information 352, and the answer rules 322 to producean answer quality level 356. For example, for each of the preliminaryanswers 354, the preliminary answer quality module 464 may compare a fitof the preliminary answer 354 to a corresponding previous answer andquestion quality level, calculate the answer quality level 356 based ona level of conformance to the answer rules 322, calculate the answerquality level 356 based on alignment with the inferred questioninformation 352, and determine the answer quality level 356 based on aninterpreted correlation with the question information 346.

FIG. 6D is a logic diagram of an embodiment of a method for producing ananswer within a computing system. In particular, a method is presentedfor use in conjunction with one or more functions and features describedin conjunction with FIGS. 1-3, 4A-4C, 5E-5F, 6C, and also FIG. 6D. Themethod includes step 480 where a processing module of one or moreprocessing modules of one or more computing devices of the computingsystem analyzes received interpreted information based on questioninformation and inferred question information to produce one or moreinterim answers. For example, the processing module generates potentialanswers based on patterns consistent with previously produced knowledgeand likelihood of correctness.

The method continues at step 482 where the processing module analyzesthe one or more interim answers based on answer rules to producepreliminary answers. For example, the processing module identifies allpossible answers from the interim answers that conform to the answerrules. The method continues at step 484 where the processing moduleanalyzes the preliminary answers in accordance with the questioninformation, the inferred question information, and the answer rules toproduce an answer quality level. For example, for each of the elementaryanswers, the processing module may compare a fit of the preliminaryanswer to a corresponding previous answer-and-answer quality level,calculate the answer quality level based on performance to the answerrules, calculate answer quality level based on alignment with theinferred question information, and determine the answer quality levelbased on interpreted correlation with the question information.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 7A is an information flow diagram for interpreting informationwithin a computing system, where sets of entigens 504 are interpretedfrom sets of identigens 502 which are interpreted from sentences ofwords 500. Such identigen entigen intelligence (IEI) processing of thewords (e.g., to IEI process) includes producing one or more of interimknowledge, a preliminary answer, and an answer quality level. Forexample, the IEI processing includes identifying permutations ofidentigens of a phrase of a sentence (e.g., interpreting humanexpressions to produce identigen groupings for each word of ingestedcontent), reducing the permutations of identigens (e.g., utilizing rulesto eliminate unfavorable permutations), mapping the reduced permutationsof identigens to at least one set of entigens (e.g., most likelyidentigens become the entigens) to produce the interim knowledge,processing the knowledge in accordance with a knowledge base (e.g.,comparing the set of entigens to the knowledge base) to produce apreliminary answer, and generating the answer quality level based on thepreliminary answer for a corresponding domain.

Human expressions are utilized to portray facts and fiction about thereal world. The real-world includes items, actions, and attributes. Thehuman expressions include textual words, textual symbols, images, andother sensorial information (e.g., sounds). It is known that many words,within a given language, can mean different things based on groupingsand orderings of the words. For example, the sentences of words 500 caninclude many different forms of sentences that mean vastly differentthings even when the words are very similar.

The present invention presents solutions where the computing system 10supports producing a computer-based representation of a truest meaningpossible of the human expressions given the way that multitudes of humanexpressions relate to these meanings. As a first step of the flowdiagram to transition from human representations of things to a mostprecise computer representation of the things, the computer identifiesthe words, phrases, sentences, etc. from the human expressions toproduce the sets of identigens 502. Each identigen includes anidentifier of their meaning and an identifier of an instance for eachpossible language, culture, etc. For example, the words car andautomobile share a common meaning identifier but have different instanceidentifiers since they are different words and are spelled differently.As another example, the word duck is associated both with a bird and anaction to elude even though they are spelled the same. In this examplethe bird duck has a different meaning than the elude duck and as sucheach has a different meaning identifier of the corresponding identigens.

As a second step of the flow diagram to transition from humanrepresentations of things to the most precise computer representation ofthe things, the computer extracts meaning from groupings of theidentified words, phrases, sentences, etc. to produce the sets ofentigens 504. Each entigen includes an identifier of a singleconceivable and perceivable thing in space and time (e.g., independentof language and other aspects of the human expressions). For example,the words car and automobile are different instances of the same meaningand point to a common shared entigen. As another example, the word duckfor the bird meaning has an associated unique entigen that is differentthan the entigen for the word duck for the elude meaning.

As a third step of the flow diagram to transition from human expressionsof things to the most precise computer representation of the things, thecomputer reasons facts from the extracted meanings. For example, thecomputer maintains a fact-based of the valid meanings from the validgroupings or sets of entigens so as to support subsequent inferences,deductions, rationalizations of posed questions to produce answers thatare aligned with a most factual view. As time goes on, and as an entigenhas been identified, it can encounter an experience transformations intime, space, attributes, actions, and words which are used to identifyit without creating contradictions or ever losing its identity.

FIG. 7B is a relationship block diagram illustrating an embodiment ofrelationships between things 510 and representations of things 512within a computing system. The things 510 includes conceivable andperceivable things including actions 522, items 524, and attributes 526.The representation of things 512 includes representations of things usedby humans 514 and representation of things used by of computing devices516 of embodiments of the present invention. The things 510 relates tothe representations of things used by humans 514 where the inventionpresents solutions where the computing system 10 supports mapping therepresentations of things used by humans 514 to the representations ofthings used by computing devices 516, where the representations ofthings used by computing devices 516 map back to the things 510.

The representations of things used by humans 514 includes textual words528, textual symbols 530, images (e.g., non-textual) 532, and othersensorial information 534 (e.g., sounds, sensor data, electrical fields,voice inflections, emotion representations, facial expressions,whistles, etc.). The representations of things used by computing devices516 includes identigens 518 and entigens 520. The representations ofthings used by humans 514 maps to the identigens 518 and the identigens518 map to the entigens 520. The entigens 520 uniquely maps back to thethings 510 in space and time, a truest meaning the computer is lookingfor to create knowledge and answer questions based on the knowledge.

To accommodate the mapping of the representations of things used byhumans 514 to the identigens 518, the identigens 518 is partitioned intoactenyms 544 (e.g., actions), itenyms 546 (e.g., items), attrenyms 548(e.g., attributes), and functionals 550 (e.g., that join and/ordescribe). Each of the actenyms 544, itenyms 546, and attrenyms 548 maybe further classified into singulatums 552 (e.g., identify one uniqueentigen) and pluratums 554 (e.g., identify a plurality of entigens thathave similarities).

Each identigen 518 is associated with an identigens identifier (IDN)536. The IDN 536 includes a meaning identifier (ID) 538 portion, aninstance ID 540 portion, and a type ID 542 portion. The meaning ID 538includes an identifier of common meaning. The instance ID 540 includesan identifier of a particular word and language. The type ID 542includes one or more identifiers for actenyms, itenyms, attrenyms,singulatums, pluratums, a time reference, and any other reference todescribe the IDN 536. The mapping of the representations of things usedby humans 514 to the identigens 518 by the computing system of thepresent invention includes determining the identigens 518 in accordancewith logic and instructions for forming groupings of words.

Generally, an embodiment of this invention presents solutions where theidentigens 518 map to the entigens 520. Multiple identigens may map to acommon unique entigen. The mapping of the identigens 518 to the entigens520 by the computing system of the present invention includesdetermining entigens in accordance with logic and instructions forforming groupings of identigens.

FIG. 7C is a diagram of an embodiment of a synonym words table 570within a computing system, where the synonym words table 570 includesmultiple fields including textual words 572, identigens 518, andentigens 520. The identigens 518 includes fields for the meaningidentifier (ID) 538 and the instance ID 540. The computing system of thepresent invention may utilize the synonym words table 570 to map textualwords 572 to identigens 518 and map the identigens 518 to entigens 520.For example, the words car, automobile, auto, bil (Swedish), carro(Spanish), and bil (Danish) all share a common meaning but are differentinstances (e.g., different words and languages). The words map to acommon meaning ID but to individual unique instant identifiers. Each ofthe different identigens map to a common entigen since they describe thesame thing.

FIG. 7D is a diagram of an embodiment of a polysemous words table 576within a computing system, where the polysemous words table 576 includesmultiple fields including textual words 572, identigens 518, andentigens 520. The identigens 518 includes fields for the meaningidentifier (ID) 538 and the instance ID 540. The computing system of thepresent invention may utilize the polysemous words table 576 to maptextual words 572 to identigens 518 and map the identigens 518 toentigens 520. For example, the word duck maps to four differentidentigens since the word duck has four associated different meanings(e.g., bird, fabric, to submerge, to elude) and instances. Each of theidentigens represent different things and hence map to four differententigens.

FIG. 7E is a diagram of an embodiment of transforming words intogroupings within a computing system that includes a words table 580, agroupings of words section to validate permutations of groupings, and agroupings table 584 to capture the valid groupings. The words table 580includes multiple fields including textual words 572, identigens 518,and entigens 520. The identigens 518 includes fields for the meaningidentifier (ID) 538, the instance ID 540, and the type ID 542. Thecomputing system of the present invention may utilize the words table580 to map textual words 572 to identigens 518 and map the identigens518 to entigens 520. For example, the word pilot may refer to a flyerand the action to fly. Each meaning has a different identigen anddifferent entigen.

The computing system the present invention may apply rules to the fieldsof the words table 580 to validate various groupings of words. Thosethat are invalid are denoted with a “X” while those that are valid areassociated with a check mark. For example, the grouping “pilot Tom” isinvalid when the word pilot refers to flying and Tom refers to a person.The identigen combinations for the flying pilot and the person Tom aredenoted as invalid by the rules. As another example, the grouping “pilotTom” is valid when the word pilot refers to a flyer and Tom refers tothe person. The identigen combinations for the flyer pilot and theperson Tom are denoted as valid by the rules.

The groupings table 584 includes multiple fields including grouping ID586, word strings 588, identigens 518, and entigens 520. The computingsystem of the present invention may produce the groupings table 584 as astored fact base for valid and/or invalid groupings of words identifiedby their corresponding identigens. For example, the valid grouping“pilot Tom” referring to flyer Tom the person is represented with agrouping identifier of 3001 and identity and identifiers 150.001 and457.001. The entigen field 520 may indicate associated entigens thatcorrespond to the identigens. For example, entigen e717 corresponds tothe flyer pilot meaning and entigen e61 corresponds to the time theperson meaning. Alternatively, or in addition to, the entigen field 520may be populated with a single entigen identifier (ENI).

The word strings field 588 may include any number of words in a string.Different ordering of the same words can produce multiple differentstrings and even different meanings and hence entigens. More broadly,each entry (e.g., role) of the groupings table 584 may refer togroupings of words, two or more word strings, an idiom, just identigens,just entigens, and/or any combination of the preceding elements. Eachentry has a unique grouping identifier. An idiom may have a uniquegrouping ID and include identifiers of original word identigens andreplacing identigens associated with the meaning of the idiom not justthe meaning of the original words. Valid groupings may still haveambiguity on their own and may need more strings and/or context toselect a best fit when interpreting a truest meaning of the grouping.

FIG. 8A is a data flow diagram for accumulating knowledge within acomputing system, where a computing device, at a time=t0, ingests andprocesses facts 598 at a step 590 based on rules 316 and fact baseinformation 600 to produce groupings 602 for storage in a fact base 592(e.g., words, phrases, word groupings, identigens, entigens, qualitylevels). The facts 598 may include information from books, archive data,Central intelligence agency (CIA) world fact book, trusted content, etc.The ingesting may include filtering to organize and promote better validgroupings detection (e.g., considering similar domains together). Thegroupings 602 includes one or more of groupings identifiers, identigenidentifiers, entigen identifiers, and estimated fit quality levels. Theprocessing step 590 may include identifying identigens from words of thefacts 598 in accordance with the rules 316 and the fact base info 600and identifying groupings utilizing identigens in accordance with rules316 and fact base info 600.

Subsequent to ingestion and processing of the facts 598 to establish thefact base 592, at a time=t1+, the computing device ingests and processesnew content 604 at a step 594 in accordance with the rules 316 and thefact base information 600 to produce preliminary grouping 606. The newcontent may include updated content (e.g., timewise) from periodicals,newsfeeds, social media, etc. The preliminary grouping 606 includes oneor more of preliminary groupings identifiers, preliminary identigenidentifiers, preliminary entigen identifiers, estimated fit qualitylevels, and representations of unidentified words.

The computing device validates the preliminary groupings 606 at a step596 based on the rules 316 and the fact base info 600 to produce updatedfact base info 608 for storage in the fact base 592. The validatingincludes one or more of reasoning a fit of existing fact base info 600with the new preliminary grouping 606, discarding preliminary groupings,updating just time frame information associated with an entry of theexisting fact base info 600 (e.g., to validate knowledge for thepresent), creating new entigens, and creating a median entigen tosummarize portions of knowledge within a median indicator as a qualitylevel indicator (e.g., suggestive not certain).

Storage of the updated fact base information 608 captures patterns thatdevelop by themselves instead of searching for patterns as in prior artartificial intelligence systems. Growth of the fact base 592 enablessubsequent reasoning to create new knowledge including deduction,induction, inference, and inferential sentiment (e.g., a chain ofsentiment sentences). Examples of sentiments includes emotion, beliefs,convictions, feelings, judgments, notions, opinions, and views.

FIG. 8B is a diagram of an embodiment of a groupings table 620 within acomputing system. The groupings table 620 includes multiple fieldsincluding grouping ID 586, word strings 588, an IF string 622 and a THENstring 624. Each of the fields for the IF string 622 and the THEN string624 includes fields for an identigen (IDN) string 626, and an entigen(ENI) string 628. The computing system of the present invention mayproduce the groupings table 620 as a stored fact base to enable IF THENbased inference to generate a new knowledge inference 630.

As a specific example, grouping 5493 points out the logic of IF someonehas a tumor, THEN someone is sick and the grouping 5494 points of thelogic that IF someone is sick, THEN someone is sad. As a result ofutilizing inference, the new knowledge inference 630 may producegrouping 5495 where IF someone has a tumor, THEN someone is possibly sad(e.g., or is sad).

FIG. 8C is a data flow diagram for answering questions utilizingaccumulated knowledge within a computing system, where a computingdevice ingests and processes question information 346 at a step 640based on rules 316 and fact base info 600 from a fact base 592 toproduce preliminary grouping 606. The ingesting and processing questionsstep 640 includes identifying identigens from words of a question inaccordance with the rules 316 and the fact base information 600 and mayalso include identifying groupings from the identified identigens inaccordance with the rules 316 and the fact base information 600.

The computing device validates the preliminary grouping 606 at a step596 based on the rules 316 and the fact base information 600 to produceidentified element information 340. For example, the computing devicereasons fit of existing fact base information with new preliminarygroupings 606 to produce the identified element information 340associated with highest quality levels. The computing device interpretsa question of the identified element information 340 at a step 642 basedon the rules 316 and the fact base information 600. The interpreting ofthe question may include separating new content from the question andreducing the question based on the fact base information 600 and the newcontent.

The computing device produces preliminary answers 354 from theinterpreted information 344 at a resolve answer step 644 based on therules 316 and the fact base information 600. For example, the computingdevice compares the interpreted information 344 two the fact baseinformation 600 to produce the preliminary answers 354 with highestquality levels utilizing one or more of deduction, induction,inferencing, and applying inferential sentiments logic. Alternatively,or in addition to, the computing device may save new knowledgeidentified from the question information 346 to update the fact base592.

FIG. 8D is a data flow diagram for answering questions utilizinginterference within a computing system that includes a groupings table648 and the resolve answer step 644 of FIG. 8C. The groupings table 648includes multiple fields including fields for a grouping (GRP)identifier (ID) 586, word strings 588, an identigen (IDN) string 626,and an entigen (ENI) 628. The groupings table 648 may be utilized tobuild a fact base to enable resolving a future question into an answer.For example, the grouping 8356 notes knowledge that Michael sleeps eighthours and grouping 8357 notes that Michael usually starts to sleep at 11PM.

In a first question example that includes a question “Michaelsleeping?”, the resolve answer step 644 analyzes the question from theinterpreted information 344 in accordance with the fact base information600, the rules 316, and a real-time indicator that the current time is 1AM to produce a preliminary answer of “possibly YES” when inferring thatMichael is probably sleeping at 1 AM when Michael usually startssleeping at 11 PM and Michael usually sleeps for a duration of eighthours.

In a second question example that includes the question “Michaelsleeping?”, the resolve answer step 644 analyzes the question from theinterpreted information 344 in accordance with the fact base information600, the rules 316, and a real-time indicator that the current time isnow 11 AM to produce a preliminary answer of “possibly NO” wheninferring that Michael is probably not sleeping at 11 AM when Michaelusually starts sleeping at 11 PM and Michael usually sleeps for aduration of eight hours.

FIG. 8E is a relationship block diagram illustrating another embodimentof relationships between things and representations of things within acomputing system. While things in the real world are described withwords, it is often the case that a particular word has multiple meaningsin isolation. Interpreting the meaning of the particular word may hingeon analyzing how the word is utilized in a phrase, a sentence, multiplesentences, paragraphs, and even whole documents or more. Describing andstratifying the use of words, word types, and possible meanings help ininterpreting a true meaning.

Humans utilize textual words 528 to represent things in the real world.Quite often a particular word has multiple instances of differentgrammatical use when part of a phrase of one or more sentences. Thegrammatical use 649 of words includes the nouns and the verbs, and alsoincludes adverbs, adjectives, pronouns, conjunctions, prepositions,determiners, exclamations, etc.

As an example of multiple grammatical use, the word “bat” in the Englishlanguage can be utilized as a noun or a verb. For instance, whenutilized as a noun, the word “bat” may apply to a baseball bat or mayapply to a flying “bat.” As another instance, when utilized as a verb,the word “bat” may apply to the action of hitting or batting an object,i.e., “bat the ball.”

To stratify word types by use, the words are associated with a word type(e.g., type identifier 542). The word types include objects (e.g., items524), characteristics (e.g., attributes 526), actions 522, and thefunctionals 550 for joining other words and describing words. Forexample, when the word “bat” is utilized as a noun, the word isdescribing the object of either the baseball bat or the flying bat. Asanother example, when the word “bat” is utilized as a verb, the word isdescribing the action of hitting.

To determine possible meanings, the words, by word type, are mapped toassociative meanings (e.g., identigens 518). For each possibleassociative meaning, the word type is documented with the meaning andfurther with an identifier (ID) of the instance (e.g., an identigenidentifier).

For the example of the word “bat” when utilized as a noun for thebaseball bat, a first identigen identifier 536-1 includes a type ID542-1 associated with the object 524, an instance ID 540-1 associatedwith the first identigen identifier (e.g., unique for the baseball bat),and a meaning ID 538-1 associated with the baseball bat. For the exampleof the word “bat” when utilized as a noun for the flying bat, a secondidentigen identifier 536-2 includes a type ID 542-1 associated with theobject 524, an instance ID 540-2 associated with the second identigenidentifier (e.g., unique for the flying bat), and a meaning ID 538-2associated with the flying bat. For the example of the word “bat” whenutilized as a verb for the bat that hits, a third identigen identifier536-2 includes a type ID 542-2 associated with the actions 522, aninstance ID 540-3 associated with the third identigen identifier (e.g.,unique for the bat that hits), and a meaning ID 538-3 associated withthe bat that hits.

With the word described by a type and possible associative meanings, acombination of full grammatical use of the word within the phrase etc.,application of rules, and utilization of an ever-growing knowledge basethat represents knowledge by linked entigens, the absolute meaning(e.g., entigen 520) of the word is represented as a unique entigen. Forexample, a first entigen e1 represents the absolute meaning of abaseball bat (e.g., a generic baseball bat not a particular baseball batthat belongs to anyone), a second entigen e2 represents the absolutemeaning of the flying bat (e.g., a generic flying bat not a particularflying bat), and a third entigen e3 represents the absolute meaning ofthe verb bat (e.g., to hit).

An embodiment of methods to ingest text to produce absolute meanings forstorage in a knowledge base are discussed in greater detail withreference to FIGS. 8F-H. Those embodiments further discuss thediscerning of the grammatical use, the use of the rules, and theutilization of the knowledge base to definitively interpret the absolutemeaning of a string of words.

Another embodiment of methods to respond to a query to produce an answerbased on knowledge stored in the knowledge base are discussed in greaterdetail with reference to FIGS. 8J-L. Those embodiments further discussthe discerning of the grammatical use, the use of the rules, and theutilization of the knowledge base to interpret the query. The queryinterpretation is utilized to extract the answer from the knowledge baseto facilitate forming the query response.

FIGS. 8F and 8G are schematic block diagrams of another embodiment of acomputing system that includes the content ingestion module 300 of FIG.5E, the element identification module 302 of FIG. 5E, the interpretationmodule 304 of FIG. 5E, the IEI control module 308 of FIG. 5E, and the SSmemory 96 of FIG. 2. Generally, an embodiment of this invention providespresents solutions where the computing system 10 supports processingcontent to produce knowledge for storage in a knowledge base.

The processing of the content to produce the knowledge includes a seriesof steps. For example, a first step includes identifying words of aningested phrase to produce tokenized words. As depicted in FIG. 8F, aspecific example of the first step includes the content ingestion module300 comparing words of source content 310 to dictionary entries toproduce formatted content 314 that includes identifiers of known words.Alternatively, when a comparison is unfavorable, the temporaryidentifier may be assigned to an unknown word. For instance, the contentingestion module 300 produces identifiers associated with the words“the”, “black”, “bat”, “eats”, and “fruit” when the ingested phraseincludes “The black bat eats fruit”, and generates the formatted content314 to include the identifiers of the words.

A second step of the processing of the content to produce the knowledgeincludes, for each tokenized word, identifying one or more identigensthat correspond the tokenized word, where each identigen describes oneof an object, a characteristic, and an action. As depicted in FIG. 8F, aspecific example of the second step includes the element identificationmodule 302 performing a look up of identigen identifiers, utilizing anelement list 332 and in accordance with element rules 318, of the one ormore identigens associated with each tokenized word of the formattedcontent 314 to produce identified element information 340.

A unique identifier is associated with each of the potential object, thecharacteristic, and the action (OCA) associated with the tokenized word(e.g. sequential identigens). For instance, the element identificationmodule 302 identifies a functional symbol for “the”, identifies a singleidentigen for “black”, identifies two identigens for “bat” (e.g.,baseball bat and flying bat), identifies a single identigen for “eats”,and identifies a single identigen for “fruit.” When at least onetokenized word is associated with multiple identigens, two or morepermutations of sequential combinations of identigens for each tokenizedword result. For example, when “bat” is associated with two identigens,two permutations of sequential combinations of identigens result for theingested phrase.

A third step of the processing of the content to produce the knowledgeincludes, for each permutation of sequential combinations of identigens,generating a corresponding equation package (i.e., candidateinterpretation), where the equation package includes a sequentiallinking of pairs of identigens (e.g., relationships), where eachsequential linking pairs a preceding identigen to a next identigen, andwhere an equation element describes a relationship between pairedidentigens (OCAs) such as describes, acts on, is a, belongs to, did, didto, etc. Multiple OCAs occur for a common word when the word hasmultiple potential meanings (e.g., a baseball bat, a flying bat).

As depicted in FIG. 8F, a specific example of the third step includesthe interpretation module 304, for each permutation of identigens ofeach tokenized word of the identified element information 340, theinterpretation module 304 generates, in accordance with interpretationrules 320 and a groupings list 334, an equation package to include oneor more of the identifiers of the tokenized words, a list of identifiersof the identigens of the equation package, a list of pairing identifiersfor sequential pairs of identigens, and a quality metric associated witheach sequential pair of identigens (e.g., likelihood of a properinterpretation). For instance, the interpretation module 304 produces afirst equation package that includes a first identigen pairing of ablack bat (e.g., flying bat with a higher quality metric level), thesecond pairing of bat eats (e.g., the flying bat eats, with a higherquality metric level), and a third pairing of eats fruit, and theinterpretation module 304 produces a second equation package thatincludes a first pairing of a black bat (e.g., baseball bat, with aneutral quality metric level), the second pairing of bat eats (e.g., thebaseball bat eats, with a lower quality metric level), and a thirdpairing of eats fruit.

A fourth step of the processing of the content to produce the knowledgeincludes selecting a surviving equation package associated with a mostfavorable confidence level. As depicted in FIG. 8F, a specific exampleof the fourth step includes the interpretation module 304 applyinginterpretation rules 320 (i.e., inference, pragmatic engine, utilizingthe identifiers of the identigens to match against known validcombinations of identifiers of entigens) to reduce a number ofpermutations of the sequential combinations of identigens to produceinterpreted information 344 that includes identification of at least oneequation package as a surviving interpretation SI (e.g., higher qualitymetric level).

Non-surviving equation packages are eliminated that compare unfavorablyto pairing rules and/or are associated with an unfavorable qualitymetric levels to produce a non-surviving interpretation NSI 2 (e.g.,lower quality metric level), where an overall quality metric level maybe assigned to each equation package based on quality metric levels ofeach pairing, such that a higher quality metric level of an equationpackage indicates a higher probability of a most favorableinterpretation. For instance, the interpretation module 304 eliminatesthe equation package that includes the second pairing indicating thatthe “baseball bat eats” which is inconsistent with a desired qualitymetric level of one or more of the groupings list 334 and theinterpretation rules 320 and selects the equation package associatedwith the “flying bat eats” which is favorably consistent with the one ormore of the quality metric levels of the groupings list 334 and theinterpretation rules 320.

A fifth step of the processing of the content to produce the knowledgeutilizing the confidence level includes integrating knowledge of thesurviving equation package into a knowledge base. For example,integrating at least a portion of the reduced OCA combinations into agraphical database to produce updated knowledge. As another example, theportion of the reduced OCA combinations may be translated into rows andcolumns entries when utilizing a rows and columns database rather than agraphical database. When utilizing the rows and columns approach for theknowledge base, subsequent access to the knowledge base may utilizestructured query language (SQL) queries.

As depicted in FIG. 8G, a specific example of the fifth step includesthe IEI control module 308 recovering fact base information 600 from SSmemory 96 to identify a portion of the knowledge base for potentialmodification utilizing the OCAs of the surviving interpretation SI 1(i.e., compare a pattern of relationships between the OCAs of thesurviving interpretation SI 1 from the interpreted information 344 torelationships of OCAs of the portion of the knowledge base includingpotentially new quality metric levels).

The fifth step further includes determining modifications (e.g.,additions, subtractions, further clarifications required wheninformation is complex, etc.) to the portion of the knowledge base basedon the new quality metric levels. For instance, the IEI control module308 causes adding the element “black” as a “describes” relationship ofan existing bat OCA and adding the element “fruit” as an eats “does to”relationship to implement the modifications to the portion of the factbase information 600 to produce updated fact base information 608 forstorage in the SS memory 96.

FIG. 8H is a logic diagram of an embodiment of a method for processingcontent to produce knowledge for storage within a computing system. Inparticular, a method is presented for use in conjunction with one ormore functions and features described in conjunction with FIGS. 1-8E,8F, and also FIG. 8G. The method includes step 650 where a processingmodule of one or more processing modules of one or more computingdevices of the computing system identifies words of an ingested phraseto produce tokenized words. The identified includes comparing words toknown words of dictionary entries to produce identifiers of known words.

For each tokenized word, the method continues at step 651 where theprocessing module identifies one or more identigens that corresponds tothe tokenized word, where each identigen describes one of an object, acharacteristic, and an action (e.g., OCA). The identifying includesperforming a lookup of identifiers of the one or more identigensassociated with each tokenized word, where he different identifiersassociated with each of the potential object, the characteristic, andthe action associated with the tokenized word.

The method continues at step 652 where the processing module, for eachpermutation of sequential combinations of identigens, generates aplurality of equation elements to form a corresponding equation package,where each equation element describes a relationship betweensequentially linked pairs of identigens, where each sequential linkingpairs a preceding identigen to a next identigen. For example, for eachpermutation of identigens of each tokenized word, the processing modulegenerates the equation package to include a plurality of equationelements, where each equation element describes the relationship (e.g.,describes, acts on, is a, belongs to, did, did too, etc.) betweensequentially adjacent identigens of a plurality of sequentialcombinations of identigens. Each equation element may be furtherassociated with a quality metric to evaluate a favorability level of aninterpretation in light of the sequence of identigens of the equationpackage.

The method continues at step 653 where the processing module selects asurviving equation package associated with most favorableinterpretation. For example, the processing module applies rules (i.e.,inference, pragmatic engine, utilizing the identifiers of the identigensto match against known valid combinations of identifiers of entigens),to reduce the number of permutations of the sequential combinations ofidentigens to identify at least one equation package, wherenon-surviving equation packages are eliminated the compare unfavorablyto pairing rules and/or are associated with an unfavorable qualitymetric levels to produce a non-surviving interpretation, where anoverall quality metric level is assigned to each equation package basedon quality metric levels of each pairing, such that a higher qualitymetric level indicates an equation package with a higher probability offavorability of correctness.

The method continues at step 654 where the processing module integratesknowledge of the surviving equation package into a knowledge base. Forexample, the processing module integrates at least a portion of thereduced OCA combinations into a graphical database to produce updatedknowledge. The integrating may include recovering fact base informationfrom storage of the knowledge base to identify a portion of theknowledge base for potential modifications utilizing the OCAs of thesurviving equation package (i.e., compare a pattern of relationshipsbetween the OCAs of the surviving equation package to relationships ofthe OCAs of the portion of the knowledge base including potentially newquality metric levels). The integrating further includes determiningmodifications (e.g., additions, subtractions, further clarificationsrequired when complex information is presented, etc.) to produce theupdated knowledge base that is based on fit of acceptable quality metriclevels, and implementing the modifications to the portion of the factbase information to produce the updated fact base information forstorage in the portion of the knowledge base.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIGS. 8J and 8K are schematic block diagrams of another embodiment of acomputing system that includes the content ingestion module 300 of FIG.5E, the element identification module 302 of FIG. 5E, the interpretationmodule 304 of FIG. 5E, the answer resolution module 306 of FIG. 5E, andthe SS memory 96 of FIG. 2. Generally, an embodiment of this inventionprovides solutions where the computing system 10 supports for generatinga query response to a query utilizing a knowledge base.

The generating of the query response to the query includes a series ofsteps. For example, a first step includes identifying words of aningested query to produce tokenized words. As depicted in FIG. 8J, aspecific example of the first step includes the content ingestion module300 comparing words of query info 138 to dictionary entries to produceformatted content 314 that includes identifiers of known words. Forinstance, the content ingestion module 300 produces identifiers for eachword of the query “what black animal flies and eats fruit and insects?”

A second step of the generating of the query response to the queryincludes, for each tokenized word, identifying one or more identigensthat correspond the tokenized word, where each identigen describes oneof an object, a characteristic, and an action (OCA). As depicted in FIG.8J, a specific example of the second step includes the elementidentification module 302 performing a look up of identifiers, utilizingan element list 332 and in accordance with element rules 318, of the oneor more identigens associated with each tokenized word of the formattedcontent 314 to produce identified element information 340. A uniqueidentifier is associated with each of the potential object, thecharacteristic, and the action associated with a particular tokenizedword. For instance, the element identification module 302 produces asingle identigen identifier for each of the black color, an animal,flies, eats, fruit, and insects.

A third step of the generating of the query response to the queryincludes, for each permutation of sequential combinations of identigens,generating a corresponding equation package (i.e., candidateinterpretation). The equation package includes a sequential linking ofpairs of identigens, where each sequential linking pairs a precedingidentigen to a next identigen. An equation element describes arelationship between paired identigens (OCAs) such as describes, actson, is a, belongs to, did, did to, etc.

As depicted in FIG. 8J, a specific example of the third step includesthe interpretation module 304, for each permutation of identigens ofeach tokenized word of the identified element information 340,generating the equation packages in accordance with interpretation rules320 and a groupings list 334 to produce a series of equation elementsthat include pairings of identigens. For instance, the interpretationmodule 304 generates a first pairing to describe a black animal, asecond pairing to describe an animal that flies, a third pairing todescribe flies and eats, a fourth pairing to describe eats fruit, and afifth pairing to describe eats fruit and insects.

A fourth step of the generating the query response to the query includesselecting a surviving equation package associated with a most favorableinterpretation. As depicted in FIG. 8J, a specific example of the fourthstep includes the interpretation module 304 applying the interpretationrules 320 (i.e., inference, pragmatic engine, utilizing the identifiersof the identigens to match against known valid combinations ofidentifiers of entigens) to reduce the number of permutations of thesequential combinations of identigens to produce interpreted information344. The interpreted information 344 includes identification of at leastone equation package as a surviving interpretation SI 10, wherenon-surviving equation packages, if any, are eliminated that compareunfavorably to pairing rules to produce a non-surviving interpretation.

A fifth step of the generating the query response to the query includesutilizing a knowledge base, generating a query response to the survivingequation package of the query, where the surviving equation package ofthe query is transformed to produce query knowledge for comparison to aportion of the knowledge base. An answer is extracted from the portionof the knowledge base to produce the query response.

As depicted in FIG. 8K, a specific example of the fifth step includesthe answer resolution module 306 interpreting the survivinginterpretation SI 10 of the interpreted information 344 in accordancewith answer rules 322 to produce query knowledge QK 10 (i.e., agraphical representation of knowledge when the knowledge base utilizes agraphical database). For example, the answer resolution module 306accesses fact base information 600 from the SS memory 96 to identify theportion of the knowledge base associated with a favorable comparison ofthe query knowledge QK 10 (e.g., by comparing attributes of the queryknowledge QK 10 to attributes of the fact base information 600), andgenerates preliminary answers 354 that includes the answer to the query.For instance, the answer is “bat” when the associated OCAs of bat, suchas black, eats fruit, eats insects, is an animal, and flies, aligns withOCAs of the query knowledge.

FIG. 8L is a logic diagram of an embodiment of a method for generating aquery response to a query utilizing knowledge within a knowledge basewithin a computing system. In particular, a method is presented for usein conjunction with one or more functions and features described inconjunction with FIGS. 1-8D, 8J, and also FIG. 8K. The method includesstep 655 where a processing module of one or more processing modules ofone or more computing devices of the computing system identifies wordsof an ingested query to produce tokenized words. For example, theprocessing module compares words to known words of dictionary entries toproduce identifiers of known words.

For each tokenized word, the method continues at step 656 where theprocessing module identifies one or more identigens that correspond tothe tokenized word, where each identigen describes one of an object, acharacteristic, and an action. For example, the processing moduleperforms a lookup of identifiers of the one or more identigensassociated with each tokenized word, where different identifiersassociated with each permutation of a potential object, characteristic,and action associated with the tokenized word.

For each permutation of sequential combinations of identigens, themethod continues at step 657 where the processing module generates aplurality of equation elements to form a corresponding equation package,where each equation element describes a relationship betweensequentially linked pairs of identigens. Each sequential linking pairs apreceding identigen to a next identigen. For example, for eachpermutation of identigens of each tokenized word, the processing moduleincludes all other permutations of all other tokenized words to generatethe equation packages. Each equation package includes a plurality ofequation elements describing the relationships between sequentiallyadjacent identigens of a plurality of sequential combinations ofidentigens.

The method continues at step 658 where the processing module selects asurviving equation package associated with a most favorableinterpretation. For example, the processing module applies rules (i.e.,inference, pragmatic engine, utilizing the identifiers of the identigensto match against known valid combinations of identifiers of entigens) toreduce the number of permutations of the sequential combinations ofidentigens to identify at least one equation package. Non-survivingequation packages are eliminated the compare unfavorably to pairingrules.

The method continues at step 659 where the processing module generates aquery response to the surviving equation package, where the survivingequation package is transformed to produce query knowledge for locatingthe portion of a knowledge base that includes an answer to the query. Asan example of generating the query response, the processing moduleinterprets the surviving the equation package in accordance with answerrules to produce the query knowledge (e.g., a graphical representationof knowledge when the knowledge base utilizes a graphical databaseformat).

The processing module accesses fact base information from the knowledgebase to identify the portion of the knowledge base associated with afavorable comparison of the query knowledge (e.g., favorable comparisonof attributes of the query knowledge to the portion of the knowledgebase, aligning favorably comparing entigens without conflictingentigens). The processing module extracts an answer from the portion ofthe knowledge base to produce the query response.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 9A is a schematic block diagram of another embodiment of acomputing system that includes trusted content sources 660, the userdevice 12-1 of FIG. 1, and the artificial intelligence (AI) server 20-1of FIG. 1. The trusted content sources 660 includes the content sources16-1 through 16-N of FIG. 1. In particular, content sources associatedwith the trusted content sources 660 provides any type of content whereat least a portion of the content has been encapsulated into a contentblockchain 662, where aspects of the content blockchain 662 aresubsequently utilized to establish an authenticity level with regards tothe content. Structure associated with the content blockchain 662 isdiscussed in greater detail with regards to FIG. 9B. The AI server 20-1includes the processing module 50-1 of FIG. 2 and the solid state (SS)memory 96 of FIG. 2. The processing module 50-1 includes the collectionsmodule 120 of FIG. 4A, the identigen entigen intelligence (IEI) module122 of FIG. 4A, and the query module 124 of FIG. 4A. Generally, anembodiment of this invention presents solutions where the computingsystem functions to verify authenticity of the content when the contentis to be utilized to create knowledge.

In an example of operation of the verifying of the authenticity, thequery module 124 interprets a received query request 136 (e.g., from theuser device 12-1) to produce query requirements that include a contentauthenticity requirement. The interpreting includes one or more ofdetermining content requirements, determining source requirements,determining answer timing requirements, determining the contentauthenticity level, and identifying at least one domain associated withthe query request 136. For example, the query module 124 determines thecontent requirements to include a query with regards to contentauthenticity, determines the source requirements to include the trustedcontent sources 660, determines the answer timing requirements toinclude up to current time, and identifies verification of a statementas the domain when receiving the query request 136 that includes aquestion “what is likelihood of [statement] being authentic?”

Having produced the query requirements, the query module 124 issues atleast one of an IEI request 244 and a collections request 132 based onthe query request 136. For example, the query module 124 generates theIEI request 244 and sends the IEI request 244 to the IEI module 122 whenthe source requirements suggest that the IEI module 122 is able toprovide an immediate response. As another example, the query module 124generates the collections request 132 and sends the collections request132 to the collections module 120 when the source requirements suggestthat a future time frame is associated with the query request 136 andmore content is required. For instance, the query module 124 issues thecollections request 132 to the collections module 120 to facilitatecollecting content over the next two hours and subsequently issues theIEI request 244 to the IEI module 122 to generate the response to thequery.

When receiving the IEI request 244, the IEI module 122 formats the IEIrequest 244 to produce human expressions that include question contentand question information. The formatting includes analyzing the IEIrequest 244 for recognizable human expressions of question content andquestion information in accordance with rules and fact base information600 (e.g., verification of authenticity of a portion of the queryrequest and/or verification of authenticity of content that generatesknowledge, where the knowledge is interpreted to provide an answer tothe query request 136) obtained from the SS memory 96.

Having produced the human expressions, the IEI module 122 applies “IEIprocessing” to the human expressions to produce one or more of newknowledge, a preliminary answer, and an answer quality level associatedwith the preliminary answer. The IEI processing includes identifyingpermutations of identigens, reducing the permutations in accordance withthe rules, mapping the reduced permutations of identigens to entigens togenerate knowledge, processing the knowledge in accordance with the factbase (e.g., mapping previously stored knowledge stored as the fact baseinfo 600 to one or more sources, where each source may be associatedwith a unique content authenticity level as revealed when interpretingan associated content blockchain 662) to produce the preliminary answer(e.g., likelihood of authenticity of content and/or a statement of thequery request), and generating the answer quality level (e.g., estimatedauthenticity level) based on the preliminary answer and the request(e.g., the IEI request 244, the query request 136).

When the answer quality level is unfavorable, the IEI module 122 issuesa collections request 132 to the collections module 120 to gather morecontent to produce knowledge to enable a desired favorable quality levelof the answer, where the quality level is associated with theauthenticity level of the content of the request and/or contentassociated with the fact base information 600. The issuing includesgenerating the collections request 132 based on one or more of the IEIrequests 244, the preliminary answer, elements of the fact baseinformation 600 (e.g., the present knowledge base), and the answerquality level.

The collections module 120 interprets one or more collections requests132 to produce content requirements. The interpreting includes one ormore of determining content selection requirements, determining sourceselection requirements, and determining content acquisition timingrequirements. For example, the collections module 120 determines thesource selection requirements to include selecting the trusted contentsources 16-1 through 16-N of the trusted content sources 660, requiringthe content blockchain 662 for newly acquired content, determining thecontent selection requirements to include content associated with thedomain and/or the question of the query request, and determining thecontent acquisition timing requirements to include the two hour timeframe.

Having produced the content requirements, the collections module 120issues a plurality of content requests 126 to a plurality of contentsources identified by the content requirements (e.g., to the contentsources 16-1 through 16-N). For example, the collections module 120identifies the plurality of trusted content sources 660, generates thecontent requests 126 based on the content requirements (e.g., to includethe content blockchain 662 in subsequent content responses 128), andsends the plurality of content requests 126 to the identified pluralityof content sources 16-1 through 16-N.

Having issued the plurality of content requests 126, the collectionsmodule 120 interprets a plurality of content responses 128 andassociated content blockchains 662 to determine whether a responsequality level is favorable. The interpreting includes analyzing theplurality of content responses 128 and validating the contentblockchains 662 to produce an estimated response quality level, andindicating a favorable response quality level when the estimatedresponse quality level compares favorably to a minimum response qualitythreshold level (e.g., a minimum threshold number of content blockchain662 have been validated). When the response quality level is favorable,the collections module 120 issues a collections response 134 to the IEImodule 122, where the collections response 134 includes further contentthat is associated with a favorable authenticity level. For example, thecollections module 120 generates the collections response 134 to includeone or more of the further content, and associated content blockchain662, and the estimated response quality level (e.g., validatedauthenticity level), and sends the collections response 134 to the IEImodule 122.

The IEI module 122 analyzes the further content based on one or more ofthe IEI request 244, the fact base information 600, and the validatedcontent blockchain 662 to produce one or more of updated fact baseinformation (e.g., new knowledge for storage in the SS memory 96 alongwith the associated content blockchain 662) and a preliminary answerwith an associated preliminary answer quality level. For example, theIEI module 122 reasons the further content with the fact baseinformation 600 to produce the preliminary answer which indicates thelikelihood of authenticity of the statement of the query request 136.When the answer quality level is favorable, the IEI module 122 issues anIEI response 246 to the query module 124 where the IEI response 246includes the preliminary answer associated with a favorable answerquality level. The query module 124 interprets the received answer toproduce a quality level of the received answer. For example, the querymodule 124 analyzes the preliminary answer in accordance with the queryrequirements and the rules to generate the quality level of the receivedanswer. When the quality level of the received answer is favorable, thequery module 124 issues a query response 140 to the user device 12-1,where the query response 140 includes the answer associated with thefavorable quality level of the answer.

FIG. 9B is a schematic block diagram of an embodiment of a contentblockchain. The content blockchain includes a plurality of blocks 2-4.Each block includes a header section and a transaction section. Theheader section includes one or more of a nonce, a hash of a precedingblock of the blockchain, where the preceding block was under control ofa preceding device (e.g., a content source, the user device, a computingdevice, etc.) in a chain of control of the blockchain, and a hash of acurrent block (e.g., a current transaction section), where the currentblock is under control of a current device in the chain of control ofthe blockchain.

The transaction section includes one or more of a public key of thecurrent device, a signature of the preceding device, authentic contentrequest information regarding a content request and change of controlfrom the preceding device to the current device, and content informationfrom the previous block as received by the previous device plus contentadded by the previous device when transferring the current block to thecurrent device.

FIG. 9B further includes devices 2-3 (e.g., content sources 16-2 and16-3) to facilitate illustration of generation of the blockchain. Eachdevice includes a hash function, a signature function, and storage for apublic/private key pair generated by the device

An example of operation of the generating of the blockchain, when thedevice 2 has control of the blockchain and is passing control of theblockchain to the device 3 (e.g., the device 3 is transacting a transferof content from device 2), the device 2 obtains the device 3 public keyfrom device 3, performs a hash function 2 over the device 3 public keyand the transaction 2 to produce a hashing resultant (e.g., precedingtransaction to device 2) and performs a signature function 2 over thehashing resultant utilizing a device 2 private key to produce a device 2signature. Having produced the device 2 signature, the device 2generates the transaction 3 to include the device 3 public key, thedevice 2 signature, device 3 authentic content request to 2 information,and the previous content plus content from device 2. The device 3authentic content request to device 2 information includes one or moreof a content request, a query request, background content, and paymentinstructions from device 3 to device 2 for access to the content. Theprevious content plus content from device 2 includes one or more ofcontent from an original source, content from any subsequent sourceafter the original source, an identifier of a source of content, aserial number of the content, an expiration date of the content, contentutilization rules, and results of previous blockchain validations.

Having produced the transaction 3 section of the block 3 a processingmodule (e.g., of the device 2, of the device 3, of a transaction miningserver, of an AI server, generates the header section by performing ahashing function over the transaction section 3 to produce a transaction3 hash, performing the hashing function over the preceding block (e.g.,block 2) to produce a block 2 hash. The performing of the hashingfunction may include generating a nonce such that when performing thehashing function to include the nonce of the header section, a desiredcharacteristic of the resulting hash is achieved (e.g., a desired numberof preceding zeros is produced in the resulting hash).

Having produced the block 3, the device 2 sends the block 3 to thedevice 3, where the device 3 initiates control of the blockchain. Havingreceived the block 3, the device 3 validates the received block 3. Thevalidating includes one or more of verifying the device 2 signature overthe preceding transaction section (e.g., transaction 2) and the device 3public key utilizing the device 2 public key (e.g., a re-createdsignature function result compares favorably to device 2 signature) andverifying that an extracted device 3 public key of the transaction 3compares favorably to the device 3 public key held by the device 3. Thedevice 3 considers the received block 3 validated when the verificationsare favorable (e.g., the authenticity of the associated content istrusted).

FIG. 9C is a logic diagram of an embodiment of a method for verifyingauthenticity of content that is utilized to create knowledge within acomputing system. In particular, a method is presented for use inconjunction with one or more functions and features described inconjunction with FIGS. 1-8L, 9A-9B, and also FIG. 9C. The methodincludes step 680 where a processing module of one or more processingmodules of one or more computing devices of the computing systeminterprets a received query request from a requester to produce contentauthenticity requirements. The interpreting includes one or more ofdetermining content requirements, (e.g., to determine an authenticitylevel of content and/or to answer a question based on knowledge, wherethe knowledge has been extracted from content associated with favorableauthenticity levels by verifying one or more content blockchainsassociated with the content), determining source requirements,determining answer timing requirements, and identifying a domainassociated with the query request.

The method continues at step 682 where the processing module IEIprocesses human expressions of the received query request based on afact base of knowledge generated from previously obtained content toproduce a preliminary answer, where the previously obtained content isassociated with a level of authenticity. The processing may includeformatting portions of the query request in accordance with formattingrules to produce recognizable human expressions of content and questioninformation. For example, the processing module produces the questioninformation to include a request to determine likelihood of authenticityof a statement for a particular domain. The processing may furtherinclude identifying permutations of identigens within the humanexpressions, reducing the permutations, mapping the reduce permutationsto entigens to produce knowledge, processing the knowledge in accordancewith the fact base of knowledge to produce the preliminary answer, andgenerating an answer quality level associated with the preliminaryanswer. For instance, the processing module generates a relatively lowanswer quality level when the luminary answer was based on knowledgederived from content associated with unfavorable authenticity levels.

When the answer quality level is unfavorable, the method continues atstep 684 where the processing module obtains further content from aplurality of content sources in accordance with the content authenticityrequirements, where authenticity of the further content is verifiableutilizing the content blockchain approach. For example, the processingmodule generates authentic content requirements. The generating of theauthentic content requirements includes determining, based on one ormore of the query requirements, the preliminary answer, and the answerquality level, one or more of content selection requirements, sourceselection requirements, a level of desired authenticity, and acquisitiontiming requirements. Having generated the authentic contentrequirements, the processing module identifies the plurality of sources(e.g., trusted content sources), generates requests based on the contentrequirements (e.g., to include requesting content blockchains withassociated content), and sends the plurality of content requests to theplurality of identified trusted content sources, analyzes a plurality ofcontent responses to verify content blockchains to produce an estimatedquality level, indicates favorable quality level when the estimatedquality level compares favorably to a minimum quality threshold level(e.g., an authenticity level is favorable when substantially all of thecontent block chains are validated), and indicates unfavorable qualitylevel to facilitate collecting more content when the estimated qualitylevel compares unfavorably to the minimum quality threshold level.

The method continues at step 686 where the processing module IEIprocesses human expressions of the further content based on the factbase to produce an updated preliminary answer that includes a queryresponse for the query request (e.g., an indication of authenticitylevel of content associated with the updated preliminary answer). Forexample, the processing module analyzes, based on one or more of thequery request, the fact base info associated with the identified domain,and the further content to produce one or more of updated fact base info(e.g., new knowledge and associated content block chains), the updatedpreliminary answer (e.g., updated response and associated authenticitylevel), and an associated answer quality level. The analyzing mayinclude reasoning the further content with the fact base to produce theupdated fact base info and the preliminary answer to include theauthenticity level.

When the updated answer quality level is favorable in accordance withthe content authenticity requirements, the method continues at step 688where the processing module issues a query response to the request(e.g., confirmation of a statement of the query request based onknowledge derived from authentic content, an answer to a question of thequery request where the answer is based on the knowledge derived fromthe authentic content). The issuing includes one or more of analyzingthe preliminary answer in accordance with the query requirements and therules to generate the updated quality level (e.g., an indication ofauthenticity levels of content relied upon to generate the knowledgethat is associated with the query response), generating the queryresponse to include the answer associated with the favorable qualitylevel, and sending the query response to the requester

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 10A is a schematic block diagram of another embodiment of acomputing system that includes clarification sources 700, the userdevice 12-1 of FIG. 1, and the artificial intelligence (AI) server 20-1of FIG. 1. The clarification sources 700 includes the content sources16-1 through 16-N of FIG. 1, the transactional servers 18-1 through 18-Nof FIG. 1, the AI servers 20-2 through 20-N of FIG. 1, and the userdevices 12-1 through 12-N of FIG. 1. Content sources associated with thetrusted content sources 660 provides any type of clarification contentincluding publicly available information, information of a domainassociated with subject matter experts, information provided byindividuals, transactional information, etc. The content is to beassociated with a topic of a statement and/or a question as furtherdiscussed below.

The AI server 20-1 includes the processing module 50-1 of FIG. 2 and thesolid state (SS) memory 96 of FIG. 2. The processing module 50-1includes the collections module 120 of FIG. 4A, the identigen entigenintelligence (IEI) module 122 of FIG. 4A, and the query module 124 ofFIG. 4A. Generally, an embodiment of this invention presents solutionswhere the computing system 10 supports the processing of an ambiguousstatement and/or query.

In an example of operation of a first embodiment of the processing ofthe ambiguous query, the query module 124 interprets a received queryrequest 136 (e.g., from the user device 12-1) to produce queryrequirements that include an indication of a statement or question type.The interpreting includes one or more of determining contentrequirements, determining source requirements, determining answer timingrequirements, and identifying at least one domain associated with thequery request 136. For example, the query module 124 determines thecontent requirements to include a query with regards to a questionand/or statement (e.g., content for ingestion to create knowledge) anddetermines the source requirements to include the clarification sources700.

The query module 124 further determines answer timing requirements toinclude up to one hour time frame, identifies a domain based on one ormore relationship aspects of the query request 136, and interprets thequery to include an ambiguous question and/or statement. For instance,the query module 124 identifies “Linda insulted Kathy and she cried”(e.g., ambiguous statement that has multiple possible interpretations).

Having produced the query requirements, the query module 124 issues atleast one of an IEI request 244 and a collections request 132 based onthe query request 136. For example, the query module 124 generates theIEI request 244 and sends the IEI request 244 to the IEI module 122 whenthe source requirements suggest that the IEI module 122 is able toprovide an immediate response. As another example, the query module 124generates the collections request 132 and sends the collections request132 to the collections module 120 when the source requirements suggestthat a future time frame is associated with the query request 136 andmore content is required. For instance, the query module 124 issues thecollections request 132 to the collections module 120 to facilitatecollecting content over the next two hours and subsequently issues theIEI request 244 to the IEI module 122 to generate the response to thequery.

When receiving the IEI request 244, the IEI module 122 formats the IEIrequest 244 to produce human expressions (e.g., text) that includequestion content and question information. The formatting includesanalyzing the IEI request 244 for recognizable human expressions of aquestion and/or content, and question information in accordance withrules and fact base information obtained from the SS memory 96. Forexample, the formatting may include producing two scenarios of humanexpressions of content, i.e., a scenario where Linda cries and anotherscenario where Kathy cries.

Having produced the human expressions, the IEI module 122 applies “IEIprocessing” to the human expressions to produce one or more of newknowledge, a preliminary answer (e.g., multiple interpretations when thestatement is ambiguous), and an answer quality level associated with thepreliminary answer (e.g., an estimated level of ambiguity). The IEIprocessing includes identifying permutations of identigens, reducing thepermutations in accordance with the rules, mapping the reducedpermutations of identigens to entigens to generate knowledge, processingthe knowledge in accordance with the fact base (e.g., mapping previouslystored knowledge stored as the fact base info 600 to one or moreinterpretations of the query) to produce the preliminary answer (e.g.,two interpretations of the statement). The IEI processing furthergenerates the answer quality level (e.g., a low level of answer qualitywhen ambiguity is detected) based on the preliminary answer and therequest (e.g., the IEI request 244, the query request 136).

When the answer quality level is unfavorable, the IEI module 122 issuesa collections request 132 to the collections module 120 to gather morecontent to produce knowledge to enable a desired favorable quality levelof the answer, where the quality level is associated with the ambiguitylevel of the statement of the request and/or content associated with thefact base information 600 (e.g., not clear from previous knowledge aboutLinda and Kathy to interpret who is likely to be crying). The issuingincludes generating the collections request 132 based on one or more ofthe IEI requests 244, the preliminary answer, elements of the fact baseinformation 600 (e.g., the present knowledge base), and the answerquality level (e.g., ambiguity level and possible interpretations of thestatement of the query request 136).

The collections module 120 interprets one or more collections requests132 to produce content requirements. The interpreting includes one ormore of determining content selection requirements, determining sourceselection requirements, and determining content acquisition timingrequirements. For example, the collections module 120 determines thesource selection requirements to include selecting one or more of thecontent sources 16-1 through 16-N when more content with regards tointeractions between Linda and Kathy, or others, may be helpful.

As another example, the collections module 120 determines the sourceselection requirements to include selecting one or more of thetransactional servers 18-1 through 18-N when transactions regardingLinda and/or Kathy may be helpful to eliminate the ambiguity. As yetanother example, the collections module 120 determines the sourceselection requirements to include selecting one or more of the userdevices 12-1 through 12-N when posing a question to another user may behelpful to remove the ambiguity (e.g., target a content request 126 forthe user device 12-1 to request clarification of who was crying, forinstance Robert witnessed Kathy crying).

Having produced the content requirements, the collections module 120issues one or more content requests 126 to at least one source entity ofthe clarification sources 700 identified by the content requirements.For example, the collections module 120 identifies the content source16-3, generates the content requests 126 based on the contentrequirements (e.g., to identify who cries more often, Linda or Kathyfrom previously generated content), and sends the content request 126 tothe content source 16-3. As another example, collections module 120identifies the user device 12-1, generates the content request 126 torequest clarification of who cried, and sends the content request 126 tothe user device 12-1.

Having issued the one or more content requests 126, the collectionsmodule 120 interprets one or more content responses 128 to determinewhether a response quality level is favorable. The interpreting includesanalyzing the one or more content responses 128 and evaluating theambiguity level based on the one or more content responses 128 toproduce an estimated response quality level. The interpreting furtherincludes indicating a favorable response quality level when theestimated response quality level compares favorably to a minimumresponse quality threshold level (e.g., the ambiguity has beensubstantially eliminated).

When the response quality level is favorable, the collections module 120issues a collections response 134 to the IEI module 122, where thecollections response 134 includes further content that is associatedwith a favorable ambiguity level. For example, the collections module120 generates the collections response 134 to include one or more of thefurther content and the estimated response quality level (e.g.,favorable ambiguity level), and sends the collections response 134 tothe IEI module 122.

The IEI module 122 analyzes the further content based on one or more ofthe IEI request 244 and knowledge of the fact base information 600 toproduce one or more of updated fact base information (e.g., newknowledge for storage in the SS memory 96) and a preliminary answer withan associated preliminary answer quality level. For example, the IEImodule 122 analyzes the further content with the fact base information600 to produce the preliminary answer which indicates the elimination ofthe ambiguity associated with the query request 136.

When the answer quality level is favorable, the IEI module 122 issues anIEI response 246 to the query module 124 where the IEI response 246includes the preliminary answer associated with a favorable answerquality level. The query module 124 interprets the received answer toproduce a quality level of the received answer. For example, the querymodule 124 analyzes the preliminary answer in accordance with the queryrequirements and the rules to generate the quality level of the receivedanswer. When the quality level of the received answer is favorable, thequery module 124 issues a query response 140 to the user device 12-1,where the query response 140 includes the answer (e.g., Kathy cried)associated with the favorable quality level of the answer.

FIGS. 10B-10C are data flow diagrams of embodiments of a method toprocess ambiguity within a computing system. Generally, embodiments ofthis invention present solutions where the computing system supports theprocessing of the ambiguous statement and/or the ambiguous query. In anexample of operation of a second embodiment of the processing of theambiguous statement and/or ambiguous query, the method includesattempting to interpret true meanings of the statement and/or query,generating a plurality of plausible entigen groups associated withplausible different meanings, obtaining cure knowledge by curating oneor more related entigen groups, updating the interpretation of thestatement and/or query through selecting at least one of the plausibleentigen groups, and generating a query response when processing a query.

The interpreting of the true meaning of the sentence for translationincludes a series of interpreting steps. FIG. 10B illustrates a firstinterpreting step that includes identifying textual words 528-1utilizing a dictionary associated with a language of the textual words.For example, the words “Linda”, “insulted”, “Kathy”, “she”, and “cried”are identified as valid words when the sentence includes: “Lindainsulted Kathy and she cried.” The example further includes a queryquestion “Who cried?”. Other embodiments include only the statement.

A second interpreting step includes identifying grammatical use 649-1(e.g., for the language, English in a specific example), where theordering of the words establishes grammatical use in accordance withnorms for the language. A third interpreting step includes identifying aword type 542 (e.g., object, characteristic, action, functional) foreach word in accordance with the language. For example, “Linda” is anobject, “insulted” is an action, “Kathy” is another object, “she” is yetanother object, and “cried” is an action.

A fourth interpreting step includes, for each word, listing possibleidentigens 518-1 (e.g., with different meanings in the first language).For example, a lookup is performing using a knowledge database thatincludes a list of all possible identigens for known words of the firstlanguage.

The detecting of the ambiguity includes generating a statement meaningentigen group 701 and, when a query is included in the textual words528-1, a query entigen group 703. The generation of the statementmeaning entigen group 701 includes selecting, for each word of thestatement portion of the textual words 528-1, a corresponding identigento produce an entigen.

The selecting includes utilizing the language rules (e.g., whichpairings, groupings, and ordering of two or more identigens are allowedin accordance with the language) to pare down the permutations ofidentigens to select the surviving entigens. For example, an entigen isselected that corresponds to Linda, an entigen corresponding to theaction of insulting is selected, an entigen is selected that correspondsto Kathy, a temporary entigen is selected for a generic she, and anentigen is selected that corresponds to the action of crying. Thetemporary entigen for the word “she” is indeterminate since it is apointer to one of the entigen for Linda and the entigen for Kathy.

Having selected the entigens, connectivity between entigens isdetermined when possible. For example, the entigen for Linda isconnected to the entigen for insult as it is clear that Linda is doingthe insulting. As another example, the insulting entigen is connected tothe entigen for Kathy as it is clear that Kathy is being insulted.However, in accordance with the language rules, including, in an absenceof clarifying punctuation, the entigen for the generic “she” does notspecifically identify how the action entigen for crying is to beconnected to the other entigens of the evolving entigen group. Since,not explicitly shown, the entigens for Linda and Kathy are associatedwith female characteristic entigens, it is deduced that one of theentigen for Linda and the entigen for Kathy are associated with thecrying as described by the temporary generic “she” entigen.

In the example, two plausible entigen groups 702-1 and 702-2 aregenerated corresponding to the two permutations of the connectivity ofthe temporary she entigen to the crying entigen. For instance, theplausible entigen group 702-1 includes the Linda entigen connected tothe insult entigen connected to the Kathy entigen connected to thecrying entigen to indicate that it is Kathy that cries. As anotherinstance, the plausible entigen group 702-2 includes the Linda entigenconnected to the insult entigen connected to the Kathy entigen, wherethe Linda entigen is further connected to the crying entigen to indicatethat it is Linda that cries.

The query entigen group 703 is generated from the query portion of thetextual words 528-1. For example, the words “who cried?” Produce thequery entigen group 703 to include a temporary entigen for “who”connected to the entigen for cry.

FIG. 10C further illustrates the example of operation where cureknowledge is obtained. The cure knowledge includes cure contextknowledge and cure interpretation capability knowledge. The cure contextknowledge includes more knowledge associated with the crying (e.g., didsomeone witness the crying that can indicate whether it was Linda orKathy). The cure interpretation capability knowledge includes moreknowledge associated with other incidents of Linda insulting otherpeople to gain insights and improve and interpretation capability. Forinstance, gain knowledge associated with other situations where Lindainsulted others and there was crying.

The obtaining of the cure knowledge includes identifying relatedknowledge and determining how to obtain the identified relatedknowledge. For example, identifying knowledge associated with a witnessof the crying. As another example, identifying knowledge associated withthe other incidents of Linda insulting other people. The obtaining ofthe identified related knowledge includes accessing a knowledge database(e.g., when the desired knowledge is expected to be previously stored inthe knowledge database) and generating of the identified relatedknowledge from further content (e.g., gather more content for IEIprocessing).

As an example of the obtaining of the cure knowledge, knowledge isobtained to generate a related entigen group 704-1 that indicates thatRobert saw Kathy cry (e.g., by retrieving the related entigen group704-1 from the knowledge database or by gathering more content togenerate the related entigen group 704-1). The related entigen groupsupports more understanding of one or more other entigen groups itrelates to.

In this example, the related entigen group 704-1, if valid, provides anindicator that Kathy cried and not Linda. As another example of theobtaining of the cure knowledge, knowledge is obtained to generateanother related entigen group 704-2 that indicates that Linda insultedPam and Pam cried. As yet another example, further knowledge is obtainedto generate yet another related entigen group 704-3 that indicates thatLinda insulted Jen and Jen cried. The related entigen group 704-2 and704-3 point to a trend of Linda insulting people where the people crynot Linda.

Having obtained the cure knowledge, the query interpretation is updatedbased on the one or more of the related entigen groups. For example,each of the three related entigen groups point to Kathy crying and notLinda such that the plausible entigen group 702-1 is deemed as correctand the plausible entigen group 702-2 is deemed as incorrect utilizingbasic deduction logic.

Having updated the query interpretation, the statement is corrected,where the correction includes at least one of generating an updatedentigen group to represent a more likely interpretation of thestatement, updating the statement (e.g., the textual words) to reflect amore accurate depiction based on the updated entigen group, and storingof the updated entigen group in the knowledge database. The generatingof the updated entigen group is based on the plausible entigen groupassociated with the more likely interpretation for example, theplausible entigen group 702-1 is utilized to generate the updatedentigen group 705-1, where the Linda entigen is connected to the insultentigen, the insult entigen is connected to the Kathy entigen and mostimportantly the Kathy entigen is connected to the cry entigen. Anexample of updating the statement includes mapping the entigens of theupdated entigen group 705-12 textual words of a desired language. Forinstance, the entigen for Linda maps to the text for Linda, the entigenfor insult maps to text for insulted, the entigen for Kathy maps to thetext for Kathy, the connectivity of the Kathy entigen to the cry entigenindicates a need to generate second text for Kathy, and the entigen forcried maps to the text for cried to produce the text: “Linda insultedKathy and Kathy cried”.

Alternatively, or in addition to, a query response 706 is generated inresponse to the query based on the updated entigen group. For example,the connectivity of the Kathy entigen to the crying entigen is utilizedto produce a query response 706 that includes the Kathy entigenconnected to the crying entigen.

FIG. 10D is a logic diagram of an embodiment of a method for processingambiguity within a computing system. In particular, a method ispresented for use in conjunction with one or more functions and featuresdescribed in conjunction with FIGS. 1-8L, 10A and also FIGS. 10B-C. Themethod includes step 710 where a processing module of one or moreprocessing modules of one or more computing devices of the computingsystem generates a plurality of entigen groups from a set of phrases ofa statement. The plurality of entigen groups represents a plurality ofmost likely meanings for the set of phrases. For example, the processingmodule receives a statement and matches, utilizing a dictionary, text ofthe statement to valid words to produce a set of identigens for eachvalid word thus producing a plurality of sets of identigens for thestatement.

The processing module applies rules (e.g., language rules) topermutations of sequences of identigens of the plurality of sets ofidentigens to reveal the plurality of entigen groups, where an entigenof an entigen group corresponds to an identigen of a set of identigenshaving a selected meaning of one or more different meanings of acorresponding word. Some of the entigen groups pertain to the same wordsand are interpreted in different ways when ambiguity exists for thestatement.

The method continues at step 711 where the processing module identifiestwo plausible entigen groups of the plurality of entigen groups based ona true meaning interpretation of the statement. The two plausibleentigen groups are substantially equally likely interpretations of thestatement based on an ambiguity in the statement. The identifying of thetwo plausible entigen groups includes interpreting, based on languagerules, the plurality of sets of identigens to produce a first plausibleentigen group of the two plausible entigen groups, where the firstplausible entigen group represents a first true meaning interpretationof the set of phrases of the statement.

A set of identigens of the plurality of sets of identigens includes oneor more different meanings of a word of the set of phrases and anentigen of the first plausible entigen group corresponds to an identigenof the set identigens having a selected meaning of the one or moredifferent meanings of the word. For example, the processing moduleidentifies the first plausible entigen group to include the Lindaentigen connected to the insult entigen connected to the Kathy entigenconnected to the cry entigen when the statement includes “Linda insultedKathy and she (Kathy) cried” when the first true meaning interpretationincludes Kathy crying

The identifying of the two plausible entigen groups further includesinterpreting, based on the language rules, the plurality of sets ofidentigens to produce a second plausible entigen group of the twoplausible entigen groups, where the second plausible entigen grouprepresents a second true meaning interpretation of the set of phrases ofthe statement. An entigen of the second plausible entigen groupcorresponds to another identigen of the set identigens having anotherselected meaning of the one or more different meanings of the word. Forexample, the processing module identifies the second plausible entigengroup to include the Linda entigen connected to the insult entigenconnected to the Kathy entigen with the Linda entigen further connectedto the cry entigen when the statement includes “Linda insulted Kathy andshe (Linda) cried” when the second true meaning interpretation includesLinda crying.

The method continues at step 712 where the processing module identifiesa related entigen group based on a phrase of the statement. Theidentifying of the related entigen group includes a series of steps. Afirst step includes selecting the phrase of the set of phrases based onthe ambiguity in the statement (e.g., she cried, Linda insulted Kathy).A second step includes identifying one or more related entigen groupattributes based on the phrase. For example, a first attribute includesLinda insulting others (e.g., find evidence of when Linda has insultedothers to identified who cries, Linda or the person being insulted) anda second attribute includes Kathy crying (e.g., find evidence of Kathycrying or not when Linda insulted Kathy).

A third step of identifying the related entigen group includes obtainingthe related entigen group by at least one of accessing a knowledgedatabase based on the one or more related entigen group attributes toobtain the related entigen group and accessing a content source toobtain content based on the one or more related entigen group attributesto enable subsequent generation of the related entigen group. Forexample, the processing module utilizes the related entigen groupattributes to search the knowledge database for the evidence of Lindainsulting others to identify who cries.

As another example, the processing module utilizes the related entigengroup attributes to search the knowledge database for evidence of Kathycrying when Linda insulted Kathy. As yet another example, the processingmodule utilizes the related entigen group attributes to select asuitable content source for the retrieval of the content to IEI processto generate the related entigen group. For instance, obtaining socialmedia content indicating other instances of Linda insulting others andthe others crying. As another instance, obtaining email contentindicating that Robert witnessed Kathy crying when Linda insulted Kathy.

Alternatively, the processing module identifies a second related entigengroup based a second phrase of the statement. For instance, the secondrelated entigen group represents a statement that Linda insulted Jen andJen cried when the related entigen group represents the statement thatRobert witnessed Kathy crying when Linda insulted Kathy.

The method branches to step 720 when an alternative embodiment isutilized where the two plausible entigen groups are augmented, otherwisethe method continues to step 713. When the method continues at step 713,the processing module interprets each of the two plausible entigengroups in light of the related entigen group to determine whether one ofthe two plausible entigen groups is a more likely interpretation of thestatement than the other one of the two plausible entigen groups. Forexample, the processing module deduces that the more likelyinterpretation is that Kathy cried rather than Linda when the relatedentigen group describes Robert witnessing Linda insulting Kathy andKathy crying.

In the other embodiment when identifying the second related entigengroup based the second phrase of the statement, the method furtherincludes interpreting each of the two plausible entigen groups in lightof the related entigen group and the second related entigen group todetermine whether the one of the two plausible entigen groups is themore likely interpretation of the statement than the other one of thetwo plausible entigen groups. For example, the processing module deducesthat the more likely interpretation is a Kathy cried rather than Lindawhen the second related entigen group indicates that Linda insulted Jenand Jen cried.

When the one of the two plausible entigen groups is the more likelyinterpretation of the statement, the method continues at step 714 wherethe processing module updates the one of the two plausible entigengroups in accordance with the related entigen group to produce anupdated entigen group. For example, the processing module re-labels therelated entigen group associated with Linda insulting Kathy and Kathycrying when the related entigen group indicates that Robert witnessKathy crying when Linda insulted Kathy. As another example, theprocessing module updates the second plausible entigen group (e.g.,where Linda cries) to move the connectivity of crying over to Kathy toproduce the updated plausible entigen group when the related entigengroup indicates that Robert witness Kathy crying when Linda insultedKathy.

In the other embodiment, when identifying a second related entigen groupbased on the second phrase of the statement, the method furtherincludes, when the one of the two plausible entigen groups is the morelikely interpretation of the statement, updating the one of the twoplausible entigen groups in accordance with the related entigen groupand the second related entigen group to produce the updated entigengroup. For example, the processing module updates the second plausibleentigen group to move the connectivity of crying over to Kathy toproduce the updated plausible entigen group when the related entigengroup includes Robert witnessing Kathy crying when Linda insulted Kathyand observing that when Linda insulted Jen, Jen cried (e.g., not Linda).

The method continues at step 715 where the processing module adds thestatement as the updated entigen group to the knowledge database. Forexample, the processing module integrates the entigens of the updatedentigen group with existing entigens of the knowledge database toproduce an updated knowledge database.

The method continues at step 716 where the processing module obtains aquery entigen group. For example, the processing module receives a querythat includes a question and IEI processes the question to produce thequery entigen group. For example, the processing module produces a queryentigen group that includes an entigen for who connected to an entigenfor cry when the question includes “Who cried” or “Who cried, Linda orKathy?” The method continues at step 717 where the processing modulegenerates a query response to the query entigen group utilizing theupdated entigen group. The query response includes one or more of aquery response entigen group and a plaintext answer. For example, theprocessing module compares the query entigen group to entigen groups ofthe updated entigen knowledge database (e.g., including the updatedentigen group) to produce a favorable match (e.g., same topic).

The processing module interprets the updated entigen group in light ofthe query entigen group to identify the query response entigen group.For instance, the processing module interprets the updated entigen grouputilizing the query entigen group to identify the Kathy entigenconnected to the cried entigen as the query response entigen group. Theprocessing module aggregates the query entigen group and a correspondingplaintext statement of “Kathy cried” to produce the query response.

When the alternative embodiment is utilized where the two plausibleentigen groups are augmented, the method continues at step 720 where theprocessing module applies the related entigen group to the firstplausible entigen group of the two plausible entigen groups to create afirst augmented entigen group. For example, the processing moduleapplies the entigen group of the statement that Robert witness Kathycrying when Linda insulted Kathy to the entigen group indicating thatLinda insulted Kathy and Kathy cried to produce the first augmentedentigen group to indicate that Linda insulted Kathy and Kathy cried(e.g., confirmation of the same).

The method continues at step 722 where the processing module appliesrelated entigen group to the second plausible entigen group of the twoplausible entigen groups to create a second augmented entigen group. Forexample, the processing module applies the entigen group of thestatement that Linda insulted Jen and Jen cried to the entigen groupindicating that Linda insulted Kathy and Linda cried to produce thesecond augmented entigen group to indicate that Linda insulted Kathy andKathy cried (e.g., switching of who cried from Linda to Kathy based onthe previous scenario when Jen cried as a result of Linda insultingJen).

The method continues at step 724 where the processing module comparesthe first augmented entigen group to the second augmented entigen groupto determine whether one of the two augmented entigen group is a morelikely interpretation of the statement than the other one of the twoaugmented entigen groups. For example, the processing module comparesthe first augmented entigen group indicating that Linda insulted Kathyand Kathy cried to the second augmented entigen group indicating (e.g.,the same), that Linda insulted Kathy and Kathy cried to indicate thateither of the two augmented entigen groups is more likely interpretationsince they are the same in this example. In other examples of thecomparison results in identification of a stronger more likelyinterpretation of the statement when subtle differences still exist evenafter the augmentation steps.

When the one of the two augmented entigen groups is the more likelyinterpretation of the statement, the method continues at step 726 wherethe processing module generates the updated entigen group based on theone of the two augmented entigen groups. For example, the processingmodule generates the updated entigen group utilizing the first augmentedentigen group to produce the updated entigen group that indicates thatLinda insulted Kathy and Kathy cried. The method branches to step 715.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element, a sixth memoryelement, a seventh memory element, etc.) that stores operationalinstructions can, when executed by one or more processing modules of oneor more computing devices (e.g., one or more servers, one or more userdevices) of the computing system 10, cause the one or more computingdevices to perform any or all of the method steps described above.

FIG. 11A is a schematic block diagram of another embodiment of acomputing system that includes a plurality of domains 1 (D1) throughdomain N (DN) content sources 730, a corresponding plurality of theartificial intelligence (AI) servers 20-1 through 20-N of FIG. 1, andthe user device 12-1 of FIG. 1. Each of the domain 1-N content sources730 includes content sources 16-1 through 16-N of FIG. 1. In particular,each of the AI servers 20-1 through 20-N is associated with acorresponding domain D1-DN of content sources 730. Each of the AIservers 20-1 through 20-N includes the processing module 50-1 of FIG. 2and the solid state (SS) memory 96 of FIG. 2, where each SS memory 96 isutilized to store associated domain knowledge. For instance, the SSmemory 96 of the AI server 20-1 is utilized to store domain 1 fact baseinformation 734, the AI server 20-2 is utilized to store domain 2 factbase information 734, etc. Each processing module 50-1 includes thecollections module 120 of FIG. 4A, the identigen entigen intelligence(IEI) module 122 of FIG. 4A, and the query module 124 of FIG. 4A.

The user device 12-1 includes the processing module 50-1 of FIG. 2 andthe solid state (SS) memory 96 of FIG. 2, where the SS memory 96 of theuser device 12-1 is utilized to store associated domain knowledge. Forinstance, the SS memory 96 of the AI server 20-1 is utilized to store aportion of one or more domains 1-N fact base information 734 ascomposite fact base information 738. The processing module 50-1 of theuser device 12-1 includes the collections module 120 of FIG. 4A, theidentigen entigen intelligence (IEI) module 122 of FIG. 4A, and thequery module 124 of FIG. 4A. Generally, an embodiment of this inventionpresents solutions where the computing system functions to optimize aknowledge base (e.g., of the user device 12-1).

In an example of operation of the optimizing of the knowledge base, theIEI module 122 of the user device 12-1 receives one or more IEI requests244 with regards to one or more query requests 136 received by the querymodule 124, where the one or more query requests 136 are associated withone or more domains. The IEI module 122 formats the one or more IEIrequests 244 to produce human expressions that includes question contentand question information associated with the one or more domains, wherethe producing of the human expressions is in accordance with expressionidentification rules. For example, the IEI module 122 analyzes the oneor more IEI requests in accordance with rules from the SS memory 96.

Having produced the human expressions, the IEI module 122 applies IEIprocessing to the human expressions to produce one or more of newknowledge, a preliminary answer, and an answer quality level. Forexample, the IEI module 122 identifies permutations of identigens,reduces the permutations, maps the reduced permutations of identigens toentigens to produce knowledge, processes the knowledge in accordancewith the local fact base of SS memory 96 to produce the preliminaryanswer and generates the answer quality level based on the preliminaryanswer for each domain.

When the answer quality level associated with one or more of the queryrequests 136 is unfavorable, the IEI module 122 identifies the domainassociated with the unfavorable answer quality level. For example, theIEI module 122 correlates the associated query request with a domainassociated with at least one of the AI servers 20-1 through 20-N. Foreach identified domain, the IEI module 122 issues a fact base request732 to one or more AI servers 20. For example, for each request, the IEImodule 122 identifies one or more of the AI servers that are associatedwith the domain, generates the fact base request 732 (i.e., to includerequester identifier (ID), target ID, domain ID, a portion of the domainindicator, security credentials), and sends the fact base request 732 toa corresponding IEI module 122 of at least one of the AI servers 20-1through 20-N.

Having sent the at least one fact base request 732, IEI module 122 ofthe user device 12-1 receives one or more corresponding fact baseresponses 736, where one or more of the AI servers 20-1 through 20-Nresponds to the one or more fact base requests 732, where each fact baseresponse 736 includes at least a portion of a fact base associated withthe corresponding AI server 20-1 through 20-N. The IEI module 122 storescomposite fact base info 738 in the SS memory 96 of the user device12-1, where the composite fact base info 738 includes at least some ofthe fact base info 734 from one or more of the AI servers 20-1 through20-N. For example, the IEI module 122 extracts fact base info 734 fromeach fact base response 736, associates the fact base info 734 with acorresponding domain when storing the composite fact base info 738 inthe SS memory 96 of the user device 12-1.

Having updated the fact base within the SS memory 96 of the user device12-1, the IEI module 122 re-applies the IEI processing to the humanexpressions to produce an updated answer and an updated answer qualitylevel, where the reapplying includes utilizing at least a portion of thecomposite fact base info 738. When the updated answer quality level isfavorable, the IEI module 122 issues an IEI response 246 to the querymodule 124 utilizing the updated answer, where the query module 124issues a query response 140 based on the IEI response 246.

FIG. 11B is a logic diagram of an embodiment of a method for optimizinga knowledge base within a computing system. In particular, a method ispresented for use in conjunction with one or more functions and featuresdescribed in conjunction with FIGS. 1-8L, 11A and also FIG. 11B. Themethod includes step 750 where a processing module of one or moreprocessing modules of one or more computing devices of the computingsystem interprets one or more received requests to produce humanexpressions of questions associated with one or more domains. Forexample, the processing module determines content requirements,determine source requirements, indicates a domain for each request,determines answer timing requirements, determines a domain associatedwith each query request, and analyzes each request in accordance withhuman expression rules to produce the human expressions.

The method continues at step 752 where the processing module applies IEIprocessing to the human expressions utilizing a local fact base toproduce one or more preliminary answer and answer quality levelgroupings. For example, the processing module identifies permutations ofidentigens, reduces the permutations, maps the reduced permutations ofidentigens to entigens to produce knowledge, processes the knowledge inaccordance with the local fact base to produce supplementary answer andgenerate the answer quality level based on the preliminary answer foreach domain.

When any answer quality level is unfavorable, the method continues atstep 754 where the processing module identifies a domain associated withthe unfavorable answer quality level. For example, the processing modulecorrelates the associated query request with a domain associated with atleast one other fact base of the computing system. For each domain, themethod continues at step 756 where the processing module obtains furtherfact base info to augment the local fact base. For example, for eachrequest, the processing module identifies one or more other fact basesthat are associated with the domain, generates a fact base request,receives one or more fact base responses, where one or more of the otherfact bases response to the one or more fact base requests, where eachresponse includes at least a portion of a fact base associated with theresponding other fact base, updates the local fact base with at leastsome received portions of the other fact bases to produce the augmentedfact base.

The method continues at step 758 where the processing module re-appliesIEI processing to the human expressions utilizing the augmented localfact base to produce one or more updated answer and answer quality levelgroupings. For example, the processing module identifies permutations ofidentigens, reduces the permutations, maps the reduced permutations ofidentigens to entigens to produce knowledge, processes the knowledge inaccordance with the augmented local fact base to produce the updatedanswer, and generates the answer quality level based on the updatedanswer for each domain. For each updated answer associated with afavorable answer quality level, the method continues at step 760 wherethe processing module outputs a response to a corresponding receivedrequest, where the response includes the updated answer.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 12A is a schematic block diagram of another embodiment of acomputing system that includes pre-crime content sources 780, theartificial intelligence (AI) server 20-1 of FIG. 1, and the user device12-1 of FIG. 1. The pre-crime content sources 780 includes the contentsources 16-1 through 16-N of FIG. 1. In particular, content sourcesassociated with pre-crime content provide one or more of historicalcrime information, real-time police response activity records, Internettraffic, Internet traffic summaries, people information (e.g., medicalrecords, court records, school records, police reports, terroristwatchlist, be on the lookout (BOLO) information, gun registration lists,group affiliations, etc.), physical world data (environmental,structural, machine, etc.), community beliefs (e.g., social media), andnews outlet information (e.g., press releases, periodicals, radiobroadcast, television news, financial market news, etc.), etc.

The AI server 20-1 includes the processing module 50-1 of FIG. 2 and thesolid state (SS) memory 96 of FIG. 2. The processing module 50-1includes the collections module 120 of FIG. 4A, the identigen entigenintelligence (IEI) module 122 of FIG. 4A, and the query module 124 ofFIG. 4A. Generally, an embodiment of this invention presents solutionswhere the computing system functions to produce a response to a queryregarding likelihood of a crime occurring (e.g., of a cyber crime, aholdup, a strong arm robbery, a carjacking, a gang shooting, arson, aburglary, etc.) based on factual interpretations of early stages of thecrime and/or likely distractions of a pre-crime sequence.

In an example of operation of the responding to the query, the querymodule 124 interprets a received query request 136 to produce queryrequirements. The interpreting includes one or more of determiningcontent requirements, determining source requirements, determininganswer timing requirements, and identifying at least one domainassociated with the query request 136. For example, the query module 124determines the content requirements to include facts that can lead toprediction of the crime, determines the source requirements to includethe pre-crime content sources 780, determines the answer timingrequirements to include a timeframe associated with the predictedoccurrence, and identifies a particular type of crime (e.g., carjacking)as the domain when receiving the query request 136 that includes aquestion “what is a temporal view of likelihood of a crime occurring?”

Having produced the query requirements, the query module 124 issues atleast one of an IEI request 244 and a collections request 132 based onthe query request 136. For example, the query module 124 generates theIEI request 244 and sends the IEI request 244 to the IEI module 122 whenthe source requirements suggest that the IEI module 122 is able toprovide an immediate response. As another example, the query module 124generates the collections request 132 and sends the collections request132 to the collections module 120 when the source requirements suggestthat a future time frame is associated with the query request 136 andmore content is required. For instance, the query module 124 issues thecollections request 132 to the collections module 120 to facilitatecollecting content over the next 60 minutes associated with a typicalpre-crime distraction of the query request 136 and subsequently issuesthe IEI request 244 to the IEI module 122 to generate the response tothe query.

When receiving the IEI request 244, the IEI module 122 formats the IEIrequest 244 to produce human expressions that include question contentand question information. The formatting includes analyzing the IEIrequest 244 for recognizable human expressions of question content andquestion information in accordance with rules and fact base information600 (e.g., facts pertaining to the crime) obtained from the SS memory96.

Having produced the human expressions, the IEI module 122 applies “IEIprocessing” to the human expressions to produce one or more of newknowledge, a preliminary answer, and an answer quality level associatedwith the preliminary answer. The IEI processing includes identifyingpermutations of identigens, reducing the permutations in accordance withthe rules, mapping the reduced permutations of identigens to entigens togenerate knowledge, processing the knowledge in accordance with the factbase (e.g., fact base info 600) to produce the preliminary answer, andgenerating the answer quality level based on the preliminary answer andthe request (e.g., the IEI request 244, the query request 136).

When the answer quality level is unfavorable, the IEI module 122 issuesa collections request 132 to the collections module 120 to gather morecontent to produce knowledge to enable a desired favorable quality levelof the answer. The issuing includes generating the collections request132 based on one or more of the IEI requests 244, the preliminaryanswer, elements of the fact base information 600 (e.g., the presentknowledge base), and the answer quality level.

The collections module 120 interprets one or more collections requests132 to produce content requirements. The interpreting includes one ormore of determining content selection requirements, determining sourceselection requirements, and determining content acquisition timingrequirements. For example, the collections module 120 determines thesource selection requirements to include selecting the content sources16-1 through 16-N of the pre-crime content sources 780, determines thecontent selection requirements to include content associated with thecrime (e.g., scenarios that are affiliated with the pre-crimedistractions and/or the crime), and determines the content acquisitiontiming requirements to include a time span for collection if any (e.g.,the next 60 minutes).

Having produced the content requirements, the collections module 120issues a plurality of content requests 126 to a plurality of contentsources identified by the content requirements (e.g., to the contentsources 16-1 through 16-N). For example, the collections module 120identifies the plurality of content sources, generates the contentrequests based on the content requirements (e.g., looking for indicatorsof pre-crime), and sends the plurality of content requests 126 to theidentified plurality of content sources.

Having issued the plurality of content requests 126, the collectionsmodule 120 interprets a plurality of content responses 128 to determinewhether a response quality level is favorable. The interpreting includesanalyzing the plurality of content responses 128 to produce an estimatedresponse quality level and indicating a favorable response quality levelwhen the estimated response quality level compares favorably to aminimum response quality threshold level (e.g., enough indicators havebeen collected to identify specific scenarios leading to one or morepotential crimes in progress). When the response quality level isfavorable, the collections module 120 issues a collections response 134to the IEI module 122, where the collections response 134 includesfurther content. For example, the collections module 120 generates thecollections response 134 to include the further content and theestimated response quality level and sends the collections response 134to the IEI module 122.

The IEI module 122 analyzes the further content based on one or more ofthe IEI request 244 and the fact base information 600 to produce one ormore of updated fact base information (e.g., new knowledge for storagein the SS memory 96) and a preliminary answer with an associatedpreliminary answer quality level. For example, the IEI module 122reasons the further content with the fact base information 600 toproduce the preliminary answer which predicts the likelihood of thecrime by identifying one or more crime scenarios and indicators ofevolution of the one or more scenarios. The evolution of the scenariosdiscussed in greater detail with reference to FIG. 12B.

When the answer quality level is favorable, the IEI module 122 issues anIEI response 246 to the query module 124 where the IEI response 246includes the preliminary answer associated with a favorable answerquality level. The query module 124 interprets the received answer toproduce a quality level of the received answer. For example, the querymodule 124 analyzes the preliminary answer in accordance with the queryrequirements and the rules to generate the quality level of the receivedanswer. When the quality level of the received answer is favorable, thequery module 124 issues a query response 140 to the user device 12-1,where the query response 140 includes the answer associated with thefavorable quality level of the answer.

FIG. 12B is a data flow diagram for providing an answer to a questionwith regards to likelihood of a crime utilizing pre-crime sequencedetection within a computing system, where a computing device of thecomputing system performs the resolve answer step 644, based on rules316, time 790 (e.g., real-time, historical time values relative tocontent collection), and fact base info 600, on content that includes anestimated value and desired range for each of n conditions for each of Ncrime sequences to produce preliminary answers 354. Each condition ofthe content describes status of an outside force that can be determinedbased on fact base info 600 (e.g., a sign of a carjacking, etc.).

The computing device compares the estimated value of the condition to adesired range (e.g., minimum/maximum of a metric) associated with thecondition to produce the status (e.g., probability of a factual elementbased on the comparison). Each sequence includes an ordered series ofconditions that are estimated to have values that compare favorably toan associated desired value range to complete the sequence (e.g.,ordering may be strict or flexible). The plurality of sequences mayinclude any number of sequences to link to the occurrence.

In an example of operation, one sequence is utilized with threeconditions to provide a likelihood of a strong arm robbery on aneighborhood clothing store, where the first condition is an Internetcapture phrase indicating discussion about a security vulnerability ofthe neighborhood clothing store, the second condition is a historicaldescription captured on the Internet with regards to how a previousstrong arm robbery was committed at the neighborhood clothing store, anda third condition is evidence of an individual associated with previoussimilar strong-arm robberies to be within a threshold geographicproximity of the neighborhood clothing store. The computing deviceobtains the content for the first through third conditions and generatesa preliminary answer 354 that indicates that the likelihood of thestrong arm robbery crime at the local clothing store is elevated.

FIG. 12C is a logic diagram of an embodiment of a method for providingan answer to a question with regards to likelihood of a crime within acomputing system. In particular, a method is presented for use inconjunction with one or more functions and features described inconjunction with FIGS. 1-8L, 12A-12B, and also FIG. 12C. The methodincludes step 800 where a processing module of one or more processingmodules of one or more computing devices of the computing systeminterprets a received query request from a requester to produce queryrequirements with regards to a crime. The interpreting includes one ormore of determining content requirements, (e.g., to gather conditions ofsequences to determine likelihood of the crime), determining sourcerequirements, determining answer timing requirements, and identifying adomain associated with the query request (e.g., a robbery, a carjacking,a cyber theft).

The method continues at step 802 where the processing module IEIprocesses human expressions of the received query request based on afact base generated from previous content to produce a preliminary crimeanswer. The processing may include formatting portions of the queryrequest in accordance with formatting rules to produce recognizablehuman expressions of content and question information. For example, theprocessing module produces the question information to include a requestto determine likelihood of the crime (e.g., identifying conditions andscenarios that lead to the crime).

The processing may further include identifying permutations ofidentigens within the human expressions, reducing the permutations,mapping the reduce permutations to entigens to produce knowledge,processing the knowledge in accordance with a fact base to produce thepreliminary answer, and generating an answer quality level associatedwith the preliminary answer. For instance, the processing modulegenerates a relatively low answer quality level when the questionrelates to gathering information over a subsequent time frame such thatmore content must be gathered to produce an answer associated with ahigher and more favorable answer quality level (e.g., start looking forvalues of conditions associated with scenarios to support answering thelikelihood of crime question).

When the answer quality level is unfavorable, the method continues atstep 804 where the processing module generates content requirements. Thegenerating of the content requirements includes determining, based onone or more of the query requirements, preliminary answer, and theanswer quality level, one or more of content selection requirements,source selection requirements, and acquisition timing requirements.

The method continues at step 806 where the processing module obtainsfurther content from a plurality of pre-crime content sources based onthe content requirements. For example, the processing module identifiesthe plurality of pre-crime content sources, generates content requestsbased on the content requirements, and sends the plurality of contentrequests to the plurality of identified pre-crime content sources.

The processing module further analyzes a plurality of content responsesto produce an estimated quality level, indicates favorable quality levelwhen the estimated quality level compares favorably to a minimum qualitythreshold level (e.g., enough content has been collected to identifytriggers that indicate one or more scenarios playing out associated withone or more crimes), and indicates unfavorable quality level tofacilitate collecting more content when the estimated quality levelcompares unfavorably to the minimum quality threshold level.

The method continues at step 808 where the processing module IEIprocesses human expressions of the further content based on the factbase to produce an updated preliminary attack answer that identifies thelikelihood of the crime. For example, the processing module analyzes,based on one or more of the query request, the fact base info associatedwith the identified domain, and the further content to produce one ormore of updated fact base info (e.g., new knowledge), the updatedpreliminary occurrence answer (e.g., likelihood of crime), and anassociated answer quality level. The analyzing may include reasoning thefurther content with the fact base to produce the updated fact base infoand the preliminary answer to include the likelihood of the crime.

When the updated answer quality level is favorable, the method continuesat step 810 where the processing module issues a query response to therequest are that predicts the likelihood of the crime. The issuingincludes one or more of analyzing the preliminary answers in accordancewith the query requirements and the rules to generate the updatedquality level, generating the query response to include the answerassociated with favorable quality level, and sending the query responseto the requester

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 13A is a schematic block diagram of another embodiment of acomputing system that includes pre-trigger content sources 820, theartificial intelligence (AI) server 20-1 of FIG. 1, and the user device12-1 of FIG. 1. The pre-trigger content sources 820 includes the contentsources 16-1 through 16-N of FIG. 1. In particular, content sourcesassociated with pre-trigger content provide one or more of historicalinformation, real-time event information, Internet traffic, Internettraffic summaries, people information (e.g., medical records, courtrecords, school records, police reports, terrorist watchlist, be on thelookout (BOLO) information, gun registration lists, group affiliations,etc.), physical world data (environmental, structural, machine, etc.),community beliefs (e.g., social media), and news outlet information(e.g., press releases, periodicals, radio broadcast, television news,financial market news, etc.), etc.

The AI server 20-1 includes the processing module 50-1 of FIG. 2 and thesolid state (SS) memory 96 of FIG. 2. The processing module 50-1includes the collections module 120 of FIG. 4A, the identigen entigenintelligence (IEI) module 122 of FIG. 4A, and the query module 124 ofFIG. 4A.

Generally, an embodiment of this invention presents solutions where thecomputing system functions to provide an answer to a question withregards to likelihood of a sequence trigger detection, where potentialpaths that lead to a trigger detection are analyzed to determine alikelihood for each path. A trigger can refer to any state transition ofa physical entity or concept where a transition has occurred (e.g.,trigger) from one state to another or from an unlikely status to alikely status. Examples of triggers includes popularity of a particularpolitical candidate has crossed below a low threshold level (e.g.,transitioning from popular to unpopular), commute time from point A topoint B exceeds a maximum desired travel time, online consumers ofregion A prefer product X over product Y by a margin of 10%, and womenare increasingly elected as CEOs of corporations based on a particularmix of business impacting and social commitment decision criteria.

In an example of operation of the providing the answer with regards tothe likelihood of the sequence trigger detection, the query module 124interprets a received query request 136 to produce query requirements.The interpreting includes one or more of determining contentrequirements, determining source requirements, determining answer timingrequirements, and identifying at least one domain associated with thequery request 136. For example, the query module 124 determines thecontent requirements with regards to pre-trigger detection, determinesthe source requirements to include the pre-trigger content sources 820,determines the answer timing requirements to include a timeframeassociated with the potential paths leading to the trigger detection,and identifies a particular type of trigger as the domain when receivingthe query request 136 that includes a question “what is a most likelyscenario and/or path for [trigger] occurring?”

Having produced the query requirements, the query module 124 issues atleast one of an IEI request 244 and a collections request 132 based onthe query request 136. For example, the query module 124 generates theIEI request 244 and sends the IEI request 244 to the IEI module 122 whenthe source requirements suggest that the IEI module 122 is able toprovide an immediate response. As another example, the query module 124generates the collections request 132 and sends the collections request132 to the collections module 120 when the source requirements suggestthat a future time frame is associated with the query request 136 andmore content is required. For instance, the query module 124 issues thecollections request 132 to the collections module 120 to facilitatecollecting content over the next seven days associated with a typicalpre-trigger question of the query request 136 and subsequently issuesthe IEI request 244 to the IEI module 122 to generate the response tothe query.

When receiving the IEI request 244, the IEI module 122 formats the IEIrequest 244 to produce human expressions that include question contentand question information. The formatting includes analyzing the IEIrequest 244 for recognizable human expressions of question content andquestion information in accordance with rules and fact base information600 (e.g., facts pertaining to the one or more paths of triggersequences) obtained from the SS memory 96. The one or more paths oftrigger sequences is discussed in greater detail with reference to FIG.13B.

Having produced the human expressions, the IEI module 122 applies “IEIprocessing” to the human expressions to produce one or more of newknowledge, a preliminary answer, and an answer quality level associatedwith the preliminary answer. The IEI processing includes identifyingpermutations of identigens, reducing the permutations in accordance withthe rules, mapping the reduced permutations of identigens to entigens togenerate knowledge, processing the knowledge in accordance with the factbase (e.g., fact base info 600) to produce the preliminary answer, andgenerating the answer quality level based on the preliminary answer andthe request (e.g., the IEI request 244, the query request 136).

When the answer quality level is unfavorable, the IEI module 122 issuesa collections request 132 to the collections module 120 to gather morecontent to produce knowledge to enable a desired favorable quality levelof the answer. The issuing includes generating the collections request132 based on one or more of the IEI requests 244, the preliminaryanswer, elements of the fact base information 600 (e.g., the presentknowledge base), and the answer quality level.

The collections module 120 interprets one or more collections requests132 to produce content requirements. The interpreting includes one ormore of determining content selection requirements, determining sourceselection requirements, and determining content acquisition timingrequirements. For example, the collections module 120 determines thesource selection requirements to include selecting the content sources16-1 through 16-N of the pre-trigger content sources 820, determines thecontent selection requirements to include content associated with thetrigger (e.g., scenarios of sequences that are affiliated with thepre-trigger distractions and/or the trigger), and determines the contentacquisition timing requirements to include a time span for collection ifany (e.g., the next seven days).

Having produced the content requirements, the collections module 120issues a plurality of content requests 126 to a plurality of contentsources identified by the content requirements (e.g., to the contentsources 16-1 through 16-N). For example, the collections module 120identifies the plurality of content sources, generates the contentrequests based on the content requirements (e.g., looking for indicatorsof a plurality of conditions within range that progress a triggersequence resulting in an indication of a valid trigger detection), andsends the plurality of content requests 126 to the identified pluralityof content sources.

Having issued the plurality of content requests 126, the collectionsmodule 120 interprets a plurality of content responses 128 to determinewhether a response quality level is favorable. The interpreting includesanalyzing the plurality of content responses 128 to produce an estimatedresponse quality level and indicating a favorable response quality levelwhen the estimated response quality level compares favorably to aminimum response quality threshold level (e.g., enough events indicatorsof events have been observed to indicate that one or more specifictrigger scenarios have detected a trigger). When the response qualitylevel is favorable, the collections module 120 issues a collectionsresponse 134 to the IEI module 122, where the collections response 134includes further content. For example, the collections module 120generates the collections response 134 to include the further contentand the estimated response quality level and sends the collectionsresponse 134 to the IEI module 122.

The IEI module 122 analyzes the further content based on one or more ofthe IEI request 244 and the fact base information 600 to produce one ormore of updated fact base information (e.g., new knowledge for storagein the SS memory 96) and a preliminary answer with an associatedpreliminary answer quality level. For example, the IEI module 122reasons the further content with the fact base information 600 toproduce the preliminary answer which predicts the likelihood of thetrigger by identifying one or more trigger scenarios and indicators ofevolution (e.g., events of the trigger scenario are within range) of theone or more scenarios. The evolution of the scenarios discussed ingreater detail with reference to FIG. 13B.

When the answer quality level is favorable, the IEI module 122 issues anIEI response 246 to the query module 124 where the IEI response 246includes the preliminary answer associated with a favorable answerquality level. The query module 124 interprets the received answer toproduce a quality level of the received answer. For example, the querymodule 124 analyzes the preliminary answer in accordance with the queryrequirements and the rules to generate the quality level of the receivedanswer. When the quality level of the received answer is favorable, thequery module 124 issues a query response 140 to the user device 12-1,where the query response 140 includes the answer associated with thefavorable quality level of the answer.

FIG. 13B is a data flow diagram for providing an answer to a questionwith regards to likelihood of a sequence trigger detection within acomputing system, where a computing device of the computing systemperforms the resolve answer step 644, based on rules 316, time 790(e.g., real-time, historical time values relative to contentcollection), and fact base info 600, on content that includes anestimated value and desired range for each of n conditions for each of Ntrigger sequences to produce preliminary answers 354. Each condition ofthe content describes status of an outside force that can be determinedbased on fact base info 600 (e.g., a sign of a shift in perception of apolitical candidate, etc.).

The computing device compares the estimated value of the condition to adesired range (e.g., minimum/maximum of a metric) associated with thecondition to produce the status (e.g., probability of a factual elementbased on the comparison). Each sequence includes an ordered series ofconditions that are estimated to have values that compare favorably toan associated desired value range to complete the sequence (e.g.,ordering may be strict or flexible). The plurality of N sequences mayinclude any number of sequences that either link to one master trigger,represent N pads for capital and triggers, or are each consideredalternative paths to a common trigger, where detecting just oneindicates that a particular path or branch to the common trigger hasbeen detected.

In an example of operation, the trigger sequence 1 is utilized withthree conditions to provide a likelihood of a shift in feeling of apolitical candidate where the first condition is an Internet capturephrase indicating discussion about the candidate versus anothercandidate, the second condition is a historical description captured onthe Internet with regards to how a previous political candidate wassuccessfully elected or defeated for a similar political position, and athird condition is evidence of voting propensity for a representativesample of the voting population with regards to a plurality ofcandidates including the candidate.

The computing device obtains the content for the first through thirdconditions of the trigger sequence 1 and generates a preliminary answer354 that indicates that the likelihood of the shift in feeling of thepolitical candidate. The indication may further include indicating thatthe path was via the trigger sequence 1 (e.g., as opposed to one or moreof the other trigger sequences which may be analyzed in parallel).

FIG. 13C is a logic diagram of an embodiment of a method for providingan answer to a question with regards to likelihood of a sequence triggerdetection within a computing system. In particular, a method ispresented for use in conjunction with one or more functions and featuresdescribed in conjunction with FIGS. 1-8L, 13A-13B, and also FIG. 13C.The method includes step 830 where a processing module of one or moreprocessing modules of one or more computing devices of the computingsystem interprets a received query request from a requester to producequery requirements with regards to a trigger. The interpreting includesone or more of determining content requirements, (e.g., to gatherconditions of one or more sequences to determine likelihood of thetrigger), determining source requirements, determining answer timingrequirements, and identifying a domain associated with the query request(e.g., a type of trigger).

The method continues at step 832 where the processing module IEIprocesses human expressions of the received query request based on afact base generated from previous content to produce a preliminarytrigger answer. The processing may include formatting portions of thequery request in accordance with formatting rules to producerecognizable human expressions of content and question information. Forexample, the processing module produces the question information toinclude a request to determine likelihood of the trigger (e.g.,identifying scenarios and conditions of the scenarios that lead to thetrigger based on previous knowledge of the trigger type).

The processing may further include identifying permutations ofidentigens within the human expressions, reducing the permutations,mapping the reduced permutations to entigens to produce knowledge,processing the knowledge in accordance with a fact base to produce thepreliminary answer, and generating an answer quality level associatedwith the preliminary answer. For instance, the processing modulegenerates a relatively low answer quality level when the questionrelates to gathering information over a subsequent time frame such thatmore content must be gathered to produce an answer associated with ahigher and more favorable answer quality level (e.g., start looking forvalues of conditions associated with scenarios to support answering thelikelihood of trigger question).

When the answer quality level is unfavorable, the method continues atstep 834 where the processing module generates content requirements. Thegenerating of the content requirements includes determining, based onone or more of the query requirements, preliminary answer, and theanswer quality level, one or more of content selection requirements,source selection requirements, and acquisition timing requirements.

The method continues at step 836 where the processing module obtainsfurther content from a plurality of pre-trigger content sources based onthe content requirements. For example, the processing module identifiesthe plurality of pre-trigger content sources, generates content requestsbased on the content requirements, and sends the plurality of contentrequests to the plurality of identified pre-trigger content sources,analyzes a plurality of content responses to produce an estimatedquality level, indicates favorable quality level when the estimatedquality level compares favorably to a minimum quality threshold level(e.g., enough content has been collected to identify in-range values ofconditions that indicate one or more scenarios playing out associatedwith one or more paths to a common trigger), and indicates unfavorablequality level to facilitate collecting more content when the estimatedquality level compares unfavorably to the minimum quality thresholdlevel.

The method continues at step 838 where the processing module IEIprocesses human expressions of the further content based on the factbase to produce an updated preliminary trigger answer that identifiesthe likelihood of the trigger. For example, the processing moduleanalyzes, based on one or more of the query request, the fact base infoassociated with the identified domain, and the further content toproduce one or more of updated fact base info (e.g., new knowledge), theupdated preliminary occurrence answer (e.g., likelihood of the trigger),and an associated answer quality level. The analyzing may includereasoning the further content with the fact base to produce the updatedfact base info and the preliminary answer to include the likelihood ofthe trigger.

When the updated answer quality level is favorable, the method continuesat step 840 where the processing module issues a query response to therequester that predicts the likelihood of the trigger. The issuingincludes one or more of analyzing the preliminary answers in accordancewith the query requirements and the rules to generate the updatedquality level, generating the query response to include the answerassociated with favorable quality level, and sending the query responseto the requester.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 14A is a schematic block diagram of another embodiment of acomputing system that includes user interface optimization sources 850,the artificial intelligence (AI) server 20-1 of FIG. 1, and the userdevice 12-1 of FIG. 1. The user interface optimization sources 850includes the content sources 16-1 through 16-N of FIG. 1 and the userdevices 12-1 through 12-N of FIG. 1. In particular, content sourcesassociated with user interface optimization content provide one or moreof publicly available information with regards to user interfaces (e.g.,approaches, parameters, etc.), historical user interface parameters,user interface test data, user interface parameters from similar userdevices, user interface parameters utilized by one or more other usersthat share at least one commonality aspect with a user of the userdevice 12-1, assessments of user interface requirements, special needsuser interfaces, language translation supporting user interfaceparameter optimizations, message board threads by users of similar userdevices where the threads include discussion for interpretation withregards to user interface information, etc.

The AI server 20-1 includes the processing module 50-1 of FIG. 2 and thesolid state (SS) memory 96 of FIG. 2. The processing module 50-1includes the collections module 120 of FIG. 4A, the identigen entigenintelligence (IEI) module 122 of FIG. 4A, and the query module 124 ofFIG. 4A.

Generally, an embodiment of this invention presents solutions where thecomputing system functions to provide an answer to a question withregards to optimizing user interface parameters, where the user deviceutilizes a user interface approach, and where the user interfaceapproach utilizes user interface parameters to provide optimizedoperation. The user interface includes a variety of interfaces includinga visual output device, a user input device, an audio output device, avisual input device, a sensor input. Examples of specific userinterfaces is discussed in more detail with reference to FIG. 3.

In an example of operation of the providing the answer with regards tothe likelihood of the sequence trigger detection, the query module 124interprets a received query request 136 to produce query requirements.The interpreting includes one or more of determining contentrequirements, determining source requirements, determining answer timingrequirements, and identifying at least one domain associated with thequery request 136. For example, the query module 124 determines thecontent requirements with regards to optimization of a user interface,determines the source requirements to include the user interfaceoptimization sources 850 and determines the answer timing requirementsto include a timeframe associated with utilization of the userinterface. The query module 124 further identifies a particular type ofuser interface as the domain when receiving the query request 136 thatincludes a question “what are optimized user interface parameters forthis user device?”

Having produced the query requirements, the query module 124 issues atleast one of an IEI request 244 and a collections request 132 based onthe query request 136. For example, the query module 124 generates theIEI request 244 and sends the IEI request 244 to the IEI module 122 whenthe source requirements suggest that the IEI module 122 is able toprovide an immediate response. As another example, the query module 124generates the collections request 132 and sends the collections request132 to the collections module 120 when the source requirements suggestthat a future time frame is associated with the query request 136 andmore content is required. For instance, the query module 124 issues thecollections request 132 to the collections module 120 to facilitatecollecting content over the next two days prior to utilization of theuser interface associated with a question of the query request 136 andsubsequently issues the IEI request 244 to the IEI module 122 togenerate the response to the query.

When receiving the IEI request 244, the IEI module 122 formats the IEIrequest 244 to produce human expressions that include question contentand question information. The formatting includes analyzing the IEIrequest 244 for recognizable human expressions of question content andquestion information in accordance with rules and fact base information600 (e.g., knowledge pertaining to user interface optimization) obtainedfrom the SS memory 96.

Having produced the human expressions, the IEI module 122 applies “IEIprocessing” to the human expressions to produce one or more of newknowledge, a preliminary answer, and an answer quality level associatedwith the preliminary answer. The IEI processing includes identifyingpermutations of identigens, reducing the permutations in accordance withthe rules, mapping the reduced permutations of identigens to entigens togenerate knowledge, processing the knowledge in accordance with the factbase (e.g., fact base info 600) to produce the preliminary answer, andgenerating the answer quality level based on the preliminary answer andthe request (e.g., the IEI request 244, the query request 136).

When the answer quality level is unfavorable, the IEI module 122 issuesa collections request 132 to the collections module 120 to gather morecontent to produce knowledge to enable a desired favorable quality levelof the answer. The issuing includes generating the collections request132 based on one or more of the IEI requests 244, the preliminaryanswer, elements of the fact base information 600 (e.g., the presentknowledge base), and the answer quality level.

The collections module 120 interprets one or more collections requests132 to produce content requirements. The interpreting includes one ormore of determining content selection requirements, determining sourceselection requirements, and determining content acquisition timingrequirements. For example, the collections module 120 determines thesource selection requirements to include selecting the content sources16-1 through 16-N of the user interface optimization sources 850,determines the content selection requirements to include contentassociated with the user interface (e.g., utilization of a similar userinterface to indicate optimized user interface parameters), anddetermines the content acquisition timing requirements to include a timespan for collection if any (e.g., the next two days).

Having produced the content requirements, the collections module 120issues a plurality of content requests 126 to a plurality of contentsources identified by the content requirements (e.g., to the contentsources 16-1 through 16-N, to other user devices). For example, thecollections module 120 identifies the plurality of content sources,generates the content requests based on the content requirements (e.g.,looking for content associated with utilization and optimization of userinterfaces), and sends the plurality of content requests 126 to theidentified plurality of content sources.

Having issued the plurality of content requests 126, the collectionsmodule 120 interprets a plurality of content responses 128 to determinewhether a response quality level is favorable. The interpreting includesanalyzing the plurality of content responses 128 to produce an estimatedresponse quality level and indicating a favorable response quality levelwhen the estimated response quality level compares favorably to aminimum response quality threshold level (e.g., enough content has beenaccumulated to reliably produce new knowledge that is relevant to theoptimization of the user interface).

When the response quality level is favorable, the collections module 120issues a collections response 134 to the IEI module 122, where thecollections response 134 includes further content. For example, thecollections module 120 generates the collections response 134 to includethe further content and the estimated response quality level and sendsthe collections response 134 to the IEI module 122.

The IEI module 122 analyzes the further content based on one or more ofthe IEI request 244 and the fact base information 600 to produce one ormore of updated fact base information (e.g., new knowledge for storagein the SS memory 96) and a preliminary answer with an associatedpreliminary answer quality level. For example, the IEI module 122reasons the further content with the fact base information 600 toproduce the preliminary answer which includes guidance for optimizationof selection of a user interface approach and/or user interfaceparameters of one or more user interface approaches.

When the answer quality level is favorable, the IEI module 122 issues anIEI response 246 to the query module 124 where the IEI response 246includes the preliminary answer associated with a favorable answerquality level. The query module 124 interprets the received answer toproduce a quality level of the received answer. For example, the querymodule 124 analyzes the preliminary answer in accordance with the queryrequirements and the rules to generate the quality level of the receivedanswer.

When the quality level of the received answer is favorable, the querymodule 124 issues a query response 140 to the user device 12-1. Thequery response 140 includes the answer (e.g., how to establish optimizeduser interface parameters for these are device 12-1) associated with thefavorable quality level of the answer.

FIG. 14B is a logic diagram of an embodiment of a method for providingan answer to a question with regards to optimizing user interfaceparameters within a computing system. In particular, a method ispresented for use in conjunction with one or more functions and featuresdescribed in conjunction with FIGS. 1-8L, 14A and also FIG. 14B. Themethod includes step 860 where a processing module of one or moreprocessing modules of one or more computing devices of the computingsystem interprets a received query request from a requester to producequery requirements with regards to optimizing user interface parameters.The interpreting includes one or more of determining contentrequirements, (e.g., to gather content associated with the optimizationof the user interface parameters), determining source requirements,determining answer timing requirements, and identifying a domainassociated with the query request (e.g., a user interface operation).

The method continues at step 862 where the processing module IEIprocesses human expressions of the received query request based on afact base generated from previous content to produce a preliminary setof user interface parameters. The processing includes formattingportions of the query request in accordance with formatting rules toproduce recognizable human expressions of content and questioninformation. For example, the processing module produces the questioninformation to include a request to determine the user interfaceparameters.

The processing further includes identifying permutations of identigenswithin the human expressions, reducing the permutations, mapping thereduced permutations to entigens to produce knowledge, processing theknowledge in accordance with a fact base to produce the preliminaryanswer, and generating an answer quality level associated with thepreliminary answer. For instance, the processing module generates arelatively low answer quality level when the question relates togathering information over a subsequent time frame such that morecontent must be gathered to produce an answer associated with a higherand more favorable answer quality level (e.g., start looking for contentassociated with utilizing a particular user interface over the next twodays).

When the answer quality level is unfavorable, the method continues atstep 864 where the processing module generates content requirements. Thegenerating of the content requirements includes determining, based onone or more of the query requirements, preliminary answer, and theanswer quality level, one or more of content selection requirements,source selection requirements, and acquisition timing requirements.

The method continues at step 866 where the processing module obtainsfurther content from a plurality of user interface optimization contentsources based on the content requirements. For example, the processingmodule identifies the plurality of user interface optimization contentsources, generates content requests based on the content requirements,and sends the plurality of content requests to the plurality ofidentified user interface optimization content sources.

The processing module analyzes a plurality of content responses toproduce an estimated quality level, indicates favorable quality levelwhen the estimated quality level compares favorably to a minimum qualitythreshold level (e.g., enough content has been collected to produce newknowledge with regards to optimization of the user interface). Theprocessing module indicates unfavorable quality level to facilitatecollecting more content when the estimated quality level comparesunfavorably to the minimum quality threshold level.

The method continues at step 868 where the processing module IEIprocesses human expressions of the further content based on the factbase to produce an updated preliminary set of user interface parameters(e.g., a preliminary answer). For example, the processing moduleanalyzes, based on one or more of the query request, the fact base infoassociated with the identified domain, and the further content toproduce one or more of updated fact base info (e.g., new knowledge), theupdated preliminary answer (e.g., the updated preliminary set of userinterface parameters), and an associated answer quality level. Theanalyzing may include reasoning the further content with the fact baseto produce the updated fact base info and the preliminary answer toinclude the updated preliminary set of user interface parameters.

When the updated answer quality level is favorable, the method continuesat step 870 where the processing module issues a query response to therequester that includes an optimized set of user interface parameters.The issuing includes one or more of analyzing the preliminary answers inaccordance with the query requirements and the rules to generate theupdated quality level, generating the query response to include theanswer associated with favorable quality level, and sending the queryresponse to the requester.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 15A is a schematic block diagram of another embodiment of acomputing system that includes artifact content sources 880, theartificial intelligence (AI) server 20-1 of FIG. 1, and the user device12-1 of FIG. 1. The artifact content sources 880 includes the contentsources 16-1 through 16-N of FIG. 1. The content sources associated withartifact content provide artifact content. Examples include ofhistorical unfavorable document artifact information, contractinformation, contract templates, contract boilerplate passages based oncontract types, social media content (e.g., message board threadsutilized by document creation and review practitioners), public recordsof property and title, contract litigation history, Internet traffic andInternet traffic summaries. Examples of artifact content further includepeople information (e.g., medical records, court records, schoolrecords, police reports, terrorist watchlist, be on the lookout (BOLO)information, gun registration lists, group affiliations, etc.), physicalworld data (environmental, structural, machine, etc.), community beliefs(e.g., social media), and news outlet information (e.g., press releases,periodicals, radio broadcast, television news, financial market news,etc.), etc.

The AI server 20-1 includes the processing module 50-1 of FIG. 2 and thesolid state (SS) memory 96 of FIG. 2. The processing module 50-1includes the collections module 120 of FIG. 4A, the identigen entigenintelligence (IEI) module 122 of FIG. 4A, and the query module 124 ofFIG. 4A.

Generally, an embodiment of this invention presents solutions where thecomputing system 10 supports providing an answer to a question withregards to identifying an undesired document artifact in a document.Examples of the document include a letter of intent, a term sheet, alease agreement, a purchase agreement, or any other agreement betweentwo or more parties, etc.

The undesired document artifact includes one or more passages that mayunfavorably conflict with each other and/or a single passage that hasunfavorable implications for at least one party associated with thedocument. For example, a legal agreement is drafted by a first party toinclude two passages that unfavorably conflict (e.g., say differentthings with regards to a term of the agreement, cause confusion, causean unfavorable outcome to another party, leave something out that isimportant) and the computing system identifies the two unfavorablyconflicting passages. The two conflicting passages may further manifestwhen a first passage is from a reference document (e.g., a knownreliable term sheet) and the second passages from a document underreview (e.g., a draft agreement). Several embodiments of the inventionare presented. A second embodiment is discussed with reference to FIGS.15C-15F.

In an example of operation of a first embodiment of the providing theanswer with regards to the identification of the undesired documentartifact, the query module 124 interprets a received query request 136to produce query requirements. The interpreting includes one or more ofdetermining content requirements, determining source requirements,determining answer timing requirements, and identifying at least onedomain associated with the query request 136.

In an example of the interpreting, the query module 124 receives thequery request 136 that includes a question “what hidden artifacts withinthis [document] may be unfavorable?” The query module 124 determines thecontent requirements with regards to document unfavorable artifactdetection, determines the source requirements to include the artifactcontent sources 880, determines the answer timing requirements toinclude a timeframe associated with completion of document review andamendments, and identifies a particular type of document as the domain.

Having produced the query requirements, the query module 124 issues atleast one of an IEI request 244 and a collections request 132 based onthe query request 136. For example, the query module 124 generates theIEI request 244 and sends the IEI request 244 to the IEI module 122 whenthe source requirements suggest that the IEI module 122 is able toprovide an immediate response. As another example, the query module 124generates the collections request 132 and sends the collections request132 to the collections module 120 when the source requirements suggestthat a future time frame is associated with the query request 136 andmore content is required. For instance, the query module 124 issues thecollections request 132 to the collections module 120 to facilitatecollecting content over the next six hours prior to completion ofreviewing and amending the document associated with the query request136 and subsequently issues the IEI request 244 to the IEI module 122 togenerate the response to the query.

When receiving the IEI request 244, the IEI module 122 formats the IEIrequest 244 to produce human expressions that include question contentand question information. The formatting includes analyzing the IEIrequest 244 for recognizable human expressions of question content andquestion information in accordance with rules and fact base information600 (e.g., knowledge pertaining to finding unfavorable artifacts withindocuments) obtained from the SS memory 96.

Having produced the human expressions, the IEI module 122 applies “IEIprocessing” to the human expressions to produce one or more of newknowledge, a preliminary answer, and an answer quality level associatedwith the preliminary answer. The IEI processing includes identifyingpermutations of identigens, reducing the permutations in accordance withrules, mapping the reduced permutations of identigens to entigens togenerate knowledge, processing the knowledge in accordance with the factbase (e.g., fact base info 600) to produce the preliminary answer, andgenerating the answer quality level (e.g., completeness measure) basedon the preliminary answer and the request (e.g., the IEI request 244,the query request 136).

When the answer quality level is unfavorable, the IEI module 122 issuesa collections request 132 to the collections module 120 to gather morecontent to produce knowledge to enable a desired favorable quality levelof the answer. The issuing includes generating the collections request132 based on one or more of the IEI requests 244, the preliminaryanswer, elements of the fact base information 600 (e.g., the presentknowledge base), and the answer quality level.

The collections module 120 interprets one or more collections requests132 to produce content requirements. The interpreting includes one ormore of determining content selection requirements, determining sourceselection requirements, and determining content acquisition timingrequirements. For example, the collections module 120 determines thesource selection requirements to include selecting the content sources16-1 through 16-N of the artifact content sources 880, determines thecontent selection requirements to include content associated with thedocument type (e.g., other documents and discovered artifacts) anddetermines the content acquisition timing requirements to include a timespan for collection if any (e.g., the next six hours).

Having produced the content requirements, the collections module 120issues a plurality of content requests 126 to a plurality of contentsources identified by the content requirements (e.g., to the contentsources 16-1 through 16-N). For example, the collections module 120identifies the plurality of content sources, generates the contentrequests based on the content requirements (e.g., looking for contentassociated with detecting undesired document artifacts), and sends theplurality of content requests 126 to the identified plurality of contentsources.

Having issued the plurality of content requests 126, the collectionsmodule 120 interprets a plurality of content responses 128 to determinewhether a response quality level is favorable. The interpreting includesanalyzing the plurality of content responses 128 to produce an estimatedresponse quality level and indicating a favorable response quality levelwhen the estimated response quality level compares favorably to aminimum response quality threshold level (e.g., enough content has beenaccumulated to reliably produce new knowledge that is relevant todetecting the undesired document artifacts).

When the response quality level is favorable, the collections module 120issues a collections response 134 to the IEI module 122, where thecollections response 134 includes further content. For example, thecollections module 120 generates the collections response 134 to includethe further content and the estimated response quality level and sendsthe collections response 134 to the IEI module 122.

The IEI module 122 analyzes the further content based on one or more ofthe IEI request 244 and the fact base information 600 to produce one ormore of updated fact base information (e.g., new knowledge for storagein the SS memory 96) and a preliminary answer with an associatedpreliminary answer quality level. For example, the IEI module 122reasons the further content with the fact base information 600 toproduce the preliminary answer which includes identification of one ormore undesired artifacts within the document and may further include asuggested remedy.

When the answer quality level is favorable, the IEI module 122 issues anIEI response 246 to the query module 124 where the IEI response 246includes the preliminary answer associated with a favorable answerquality level. The query module 124 interprets the received answer toproduce a quality level of the received answer. For example, the querymodule 124 analyzes the preliminary answer in accordance with the queryrequirements and the rules to generate the quality level of the receivedanswer. When the quality level of the received answer is favorable, thequery module 124 issues a query response 140 to the user device 12-1,where the query response 140 includes the answer (e.g., identificationof the undesired document artifacts) associated with the favorablequality level of the answer.

FIG. 15B is a data flow diagram for the example of operation of thefirst embodiment of identifying an undesired document artifact within acomputing system. The data flow diagram includes the IEI module 122 ofFIG. 15A and fact base information 600 in the form of content sources890. The content sources 890 includes a plurality of artifact sourcesA1-AN groupings table 892. Each groupings table 892 includes multiplefields including fields for a group (GRP) identifier (ID) 586, wordstrings 588, identigen (IDN) string 626, and an entigen (ENI) 628. Forinstance, the groupings tables 892 of the content sources 890 includesword strings and identifiers associated with examples of undesireddocument artifacts such as which party compensates which other party andhow much.

As an example of operation of providing an answer to a query, the IEImodule 122 interprets the IEI request 244, facilitates obtaining thefact base information 600, and generates the preliminary answer based onthe rules 316 and associated time frames relevant to the question of theIEI request 244. For example, the IEI module 122 generates thepreliminary answer to indicate that “unfavorable artifacts includeuncertainty of A compensating B, a conflicting compensation amount, anda conflicting delivery date”.

FIGS. 15C-15E are further data flow diagrams for examples of operationof a second embodiment of identifying the undesired document artifactwithin the computing system. The second embodiment includes high-levelsteps of obtaining raw content (source content 310), generating entigengroups for meanings of phrases of related topics in step 894,identifying entigen groups with a meaning difference in step 896 andproviding difference abatement in step 898. Each of the steps will bediscussed in greater detail with reference to FIGS. 15C-15F.

FIG. 15C illustrates an example of operation of the second embodiment ofthe identifying the undesired document artifact where the raw content310 is obtained where the raw content includes one or more documents ofat least some related topics. For example, a reference document and adocument under review (e.g., a rental term sheet and a draft rentalagreement). As another example, a single document with many passagesdiscussing a related topic (e.g., the draft rental agreement). As yetanother example, two or more documents that pertain to a related topic(e.g., two versions of a draft rental agreement). Many more permutationsof documents are possible where the documents include a plurality ofphrases for analysis.

The step 894 generates entigen groups for meanings of the phrases of therelated topic. The generating includes comparing words of each phrase tocharacters of a dictionary (e.g., of a particular language) to identifyvalid words. The identifying includes, for each word, identifying,utilizing an identigens list, a set of identigens that correspond todifferent meanings of same word. Identigen ordering rules, of theparticular language, are applied to each set of identigens for adjacentidentigens and strings of two or more identigens to determine validsequences of adjacent and sequential identigens to select valid entigensfor each set of identigens to produce an entigen group for each phraseor portion of a phrase. Each entigen group represents a meaning of aphrase.

Having generated the entigen groups, the step 896 identifies the entigengroups with a meaning difference for two or more related topic phrases.For example, a first entigen group that corresponds to a first phrase(e.g., which could be a phrase under review) is compared to a secondentigen group of a second phrase (e.g., which could be a referencephrase) to produce the meaning difference. The meaning differenceincludes a conflict when meanings of entigens of the two or more entigengroups are different. The meaning difference further includes missingportions of one of the phrases compared to the other phrase when one ormore entigens are missing from an entigen group that is similar toanother entigen group.

Having identified the entigen groups with the meaning difference, thestep 898 provides the difference abatement. The difference abatementincludes one or more of updating phrases that are conflicting or missinginformation and updating entigen groups that correspond to the phrasesfor updating. The difference abatement further includes one or more ofindicating the meaning difference and indicating the conflicting ormissing information with reference to the one or more phrases that haveconflicting or missing information. A specific example of operation isdiscussed in greater detail with reference to FIG. 15D for a conflictinginformation scenario and another specific example of operation isdiscussed in greater detail with reference to FIG. 15E for a missinginformation scenario.

FIG. 15D illustrates another example of operation of the secondembodiment of the identifying the undesired document artifact where theundesired document artifact is conflicting information. In the example,a draft rental agreement includes a first phrase “The renter shall paythe landlord rent of $1000 every month.”, a second phrase “The landlordis to be paid the rent every four weeks”, and a third phrase “Thelandlord shall be paid a rent of USD $1000.

The step 894 generates first, second and third entigen groups from thefirst second and third phrases. The first entigen group is generatedfrom the first phrase where the first entigen group includes a renterentigen connected to a pays rent entigen which is connected to alandlord entigen. A $1000 characteristic entigen is connected to thepays rent entigen and a monthly characteristic entigen is connected tothe pays rent entigen.

The second entigen group is generated from the second phrase where thesecond entigen group includes a paid rent entigen connected to thelandlord entigen and a every four weeks characteristic entigen connectedto the paid rent entigen. The third entigen group is generated from thethird phrase where the third entigen group includes a $1000characteristic entigen connected to the paid rent entigen which isconnected to the landlord entigen.

The step 896 identifies the entigen groups with the meaning difference.For example, the first and second entigen groups have similar, but notidentical, meaning since a landlord is being paid rent in both entigengroups. The meaning difference is with regards to the characteristicentigens describing the pain of the rent. For example, the first entigengroup describes the rent characteristic as monthly while the secondentigen group describes the rent characteristic as every four weeks.While similar, monthly is not identical to every four weeks.

The step 898 provides the difference abatement where the words of thefirst and second conflicting phrases are identified that conflict. Forexample, the word “month” of the first phrase is identified to be inconflict with the words “4 weeks” of the second phrase.

FIG. 15E illustrates another example of operation of the secondembodiment of the identifying the undesired document artifact where theundesired document artifact is the missing information. In the example,a rental agreement term sheet (e.g., a reference document) includes areference first phrase “rent: $1000, period: monthly, payer: renter,payee: landlord, term: 12 months.” Further in the example, a draftrental agreement includes a second phrase “The renter shall pay thelandlord rent of $1000 every month”.

The step 894 generates a reference entigen group 1 and an entigen group2 respectively from reference phrase 1 and the phrase 2. The referenceentigen group 1 is generated from the reference phrase 1 to include arenter entigen connected to a pays rent entigen which is connected to alandlord entigen. A $1000 characteristic entigen is connected to thepays rent entigen, a monthly characteristic entigen is connected to thepays rent entigen, and a for 12 months characteristic entigen isconnected to the pays rent entigen.

The entigen group 2 is generated from the phrase 2 to include the renterentigen connected to the pays rent entigen connected to the landlordentigen. The entigen group 2 further includes a $1000 characteristicentigen connected to the pays rent entigen, and a monthly characteristicentigen connected to the pays rent entigen.

The step 896 identifies the entigen groups with the meaning difference.For example, the reference entigen group 1 and entigen group 2 havesimilar, but not identical, meaning since a landlord is being paid rentin both entigen groups of $1000 on a monthly basis. The meaningdifference is with regards to the term characteristic entigen present inthe reference entigen group 1 but missing from the entigen group 2.While similar, the entigen group 2 of the draft rental agreement ismissing the term characteristic for 12 months.

The step 898 provides the difference abatement where the words of thefirst and second phrases of the missing information are identified. Forexample, the words “for a term of 12 months etc.” of the first phrase ismissing from words of the phrase 2.

FIG. 15F is a logic diagram of the second embodiment of the methodidentifying an undesired document artifact within a computing system. Inparticular, a method is presented for use in conjunction with one ormore functions and features described in conjunction with FIGS. 1-8L,15A-15E, and also FIG. 15F. The method includes step 900 where aprocessing module of one or more processing modules of one or morecomputing devices of the computing system generates a plurality ofentigen groups from a plurality of phrases of a related topic (e.g., therental agreement).

The plurality of entigen groups represents most likely meanings of theplurality of phrases. For example, the generating includes generating afirst entigen group from a first phrase of a first document and a secondentigen group from a second phrase of the first document (e.g., whenlooking for conflicts within a common document). In another example, thegenerating includes generating the first entigen group from the firstphrase of the first document and the second entigen group from a secondphrase of a second document (e.g., when looking for missing informationor conflicts between a reference document in a document under review).

The method continues at step 902 where the processing module identifiesthe first entigen group and the second entigen group of the plurality ofentigen groups. A first meaning of the first entigen group is similar toa second meaning of the second entigen group. The first meaning issimilar to, but not identical to, the second meaning. In an alternativeembodiment, the identifying further includes identifying a third entigengroup of the plurality of entigen groups. A third meaning of the thirdentigen group is similar to the first meaning of the first entigen groupand to the second meaning of the second entigen group. The third meaningis similar to, but not identical to, the first meaning and the secondmeaning.

The method continues at step 904 where the processing module determinesa meaning difference between the first meaning and the second meaning(e.g., conflicting meanings, missing meanings). For example, theprocessing module compares entigens of the first entigen group toentigens of the second entigen group to identify the meaning difference(e.g., different entigens of different meanings, missing entigens). Whenthe embodiment includes the identifying of the third entigen group, thedetermining of the meaning difference further includes determining themeaning difference between each of the third meaning, the first meaning,and the second meaning.

The method continues at step 906 where the processing module determinesa meaning priority for the meanings. The determining of the meaningpriority includes a variety of approaches. A first approach todetermining the meaning priority includes, when the second phrase has aphrase under review status and the first phrase has a reference phrasestatus, indicating the first meaning has the priority over the secondmeaning, where the second entigen group is generated from the secondphrase and the first entigen group is generated from the first phrase.

A second approach to determining the meaning priority includes, when thefirst phrase has the phrase under review status and the second phrasehas the reference phrase status, indicating the second meaning has thepriority over the first meaning. A third approach to determining themeaning priority includes, when the first entigen group compares morefavorably to the third entigen group of the plurality of entigen groupsthan the second entigen group compares to the third entigen group,indicating the first meaning has the priority over the second meaning,where the third meaning of the third entigen group is similar to, butnot identical to, the first meaning.

A fourth approach to determining the meaning priority includes, when thesecond entigen group compares more favorably to the third entigen groupthan the second entigen group compares to the third entigen group,indicating the second meaning has the priority over the first meaning.The third meaning of the third entigen group is similar to, but notidentical to, the second meaning.

The method branches to step 912 when the second meaning has the priorityover the first meaning. The method branches to step 916 when the thirdmeaning has the priority over the first meaning and the second meaning.The method continues to step 908 when the first meaning has the priorityover the second meaning.

When the first meaning has priority over the second meaning, the methodcontinues at step 908 where the processing module updates the secondentigen group in accordance with the meaning difference to produce anupdated second entigen group. The updating of the second entigen groupusing the updated second meaning to produce the updated second entigengroup includes a variety of approaches.

A first approach to updating of the second entigen group includescombining the second meaning and a priority portion of the first meaningin accordance with the meaning difference to produce the updated secondmeaning. For example, the “four weeks” is replaced with “monthly” in theconflicting rental agreement example. As another example, the “12 month”term is added in the missing information rental agreement example.

A second approach to updating of the second entigen group includesreceiving, in response to a request for the updated second meaning, theupdated second meaning, where the request for the updated second meaningincludes the meaning difference. For example, the processing moduleissues the request to a responding entity (e.g., a user device) andreceives the response from the responding entity with guidance in theform of the updated second meaning. For instance, the processing modulereceives guidance to utilize the “monthly” meaning rather than the “fourweeks” meaning from the responding entity.

A third approach to updating of the second entigen group includesinterpreting the meaning difference to produce the updated secondmeaning. For example, the processing module compares the “monthly”meaning and the “four weeks meaning” to each other and/or two meaningsfrom other rental agreements to determine that the “monthly” meaning isa more appropriate updated second meaning.

The method continues at step 910 where the processing module updates thesecond phrase utilizing the updated second entigen group. The pluralityof phrases includes the second phrase and the second entigen groupcorresponds to the second phrase. For example, the processing modulegenerates text for the second phrase using the updated second entigengroup to include “the landlord is to be paid the rent every month” inthe rental agreement example.

When the second meaning has priority over the first meaning, the methodcontinues at step 912 where the processing module updates the firstentigen group in accordance with the meaning difference to produce anupdated first entigen group. The updating of the first entigen groupincludes utilizing an updated first meaning by a variety of approaches.A first approach includes combining the first meaning and a priorityportion of the second meaning in accordance with the meaning differenceto produce the updated first meaning. A second approach includesreceiving, in response to a request for the updated first meaning, theupdated first meaning, where the request for the updated first meaningincludes the meaning difference. A third approach includes interpretingthe meaning difference to produce the updated first meaning.

The method continues at step 914 where the processing module updates thefirst phrase utilizing the updated first entigen group. The plurality ofphrases includes the first phrase and the first entigen groupcorresponds to the first phrase.

When the embodiment includes the identifying of the third entigen group,and when the third meaning has priority over the first meaning and thesecond meaning, the method continues at step 916 where the processingmodule updates the first entigen group in accordance with the meaningdifference to produce the updated first entigen group and updates thesecond entigen group in accordance with the meaning difference toproduce the updated second entigen group. For example, in the rentalagreement example, when a further term sheet reference document includesthe third phrase that produces the third entigen group specifying thatthe word “tenant” shall be utilized rather than “renter”, the processingmodule updates the first and second entigen groups to portray meaningsincluding “tenant” rather than “renter”.

The method continues at step 918 where the processing module updates thefirst phrase utilizing the updated first entigen group and updates thesecond phrase utilizing the updated second entigen group. For example,the processing module generates the updated first phrase of “The tenantshall pay the landlord rent of $1000 every month” and generates theupdated second phrase of “The landlord shall receive the rent every fourweeks from the tenant”.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 16A is a schematic block diagram of another embodiment of acomputing system that includes trusted content sources 938, theartificial intelligence (AI) server 20-1 of FIG. 1, and the user device12-1 of FIG. 1. The trusted content sources 938 includes the contentsources 16-1 through 16-N of FIG. 1. In particular, content sourcesassociated with trusted content provide one or more of contentassociated with historically favorable accuracy levels of content (e.g.,more likely true than not), known levels of fact worthiness, known to beable to extract knowledge from content, etc.

The AI server 20-1 includes the processing module 50-1 of FIG. 2 and thesolid state (SS) memory 96 of FIG. 2. The processing module 50-1includes the collections module 120 of FIG. 4A, the identigen entigenintelligence (IEI) module 122 of FIG. 4A, and the query module 124 ofFIG. 4A. the SS memory 96 includes a single type database 932 (e.g., asingle document type format) and a graphical database 934 (e.g., wheredata is organized in a graphical database format). Generally, anembodiment of this invention presents solutions where the computingsystem functions to utilize multiple database formats when creatingknowledge from content, where the multiple database formats includes thegraphical database format.

In an example of operation of the utilizing of the multiple databaseformats when creating knowledge from content the query module 124interprets a received query request 136 to produce query requirementswith regards to trusted content. The interpreting includes one or moreof determining a requirement to generate new knowledge, determiningcontent requirements, determining source requirements, determininganswer timing requirements, and identifying at least one domainassociated with the query request 136. For example, the query module 124determines the content requirements with regards to creating newknowledge stored by multiple database formats, determines the sourcerequirements to include the trusted content sources 938, determines theanswer timing requirements to include a timeframe associated with thecollection of content, and identifies a domain of the query request 136when receiving the query request 136.

Having produced the query requirements, the query module 124 issues atleast one of an IEI request 244 and a collections request 132 based onthe query request 136. For example, the query module 124 generates theIEI request 244 and sends the IEI request 244 to the IEI module 122 whenthe source requirements suggest that the IEI module 122 is able toprovide an immediate response. As another example, the query module 124generates the collections request 132 and sends the collections request132 to the collections module 120 when the source requirements suggestthat a future time frame is associated with the query request 136 andmore content is required. For instance, the query module 124 issues thecollections request 132 to the collections module 120 to facilitatecollecting content over the next 10 minutes for the domain associatedwith the query request 136 and subsequently issues the IEI request 244to the IEI module 122 to generate the response to the query.

When receiving the IEI request 244, the IEI module 122 formats the IEIrequest 244 to produce human expressions that include question contentand question information. The formatting includes analyzing the IEIrequest 244 for recognizable human expressions of question content andquestion information in accordance with rules 316 maintained in thesingle type database 932 and fact base information 600 maintained in thegraphical database 934 (e.g., knowledge pertaining to the domain of thequery request 136) obtained from the SS memory 96.

Having produced the human expressions, the IEI module 122 applies “IEIprocessing” to the human expressions to produce one or more of newknowledge, a preliminary answer, and an answer quality level associatedwith the preliminary answer. The IEI processing includes identifyingpermutations of identigens, reducing the permutations in accordance withthe rules, mapping the reduced permutations of identigens to entigens togenerate knowledge, processing the knowledge in accordance with the factbase 600 (e.g., from the graphical database 934) to produce thepreliminary answer where a most favorable graphic representation of theanswer is detected, and generating the answer quality level based on thepreliminary answer and the request (e.g., the IEI request 244, the queryrequest 136).

When the answer quality level is unfavorable (e.g., ambiguous), the IEImodule 122 issues a collections request 132 to the collections module120 to gather more content to produce knowledge to enable a desiredfavorable quality level of the answer. The issuing includes generatingthe collections request 132 based on one or more of the IEI requests244, the preliminary answer, elements of the fact base information 600(e.g., the present knowledge base), the rules 316, and the answerquality level.

The collections module 120 interprets one or more collections requests132 to produce content requirements. The interpreting includes one ormore of determining content selection requirements, determining sourceselection requirements, and determining content acquisition timingrequirements. For example, the collections module 120 determines thesource selection requirements to include selecting the content sources16-1 through 16-N of the trusted content sources 938, determines thecontent selection requirements to include content associated with thedomain of the query request 136 (e.g., topics) and determines thecontent acquisition timing requirements to include a time span forcollection if any (e.g., the next 10 minutes).

Having produced the content requirements, the collections module 120issues a plurality of content requests 126 to a plurality of contentsources identified by the content requirements (e.g., to the contentsources 16-1 through 16-N). For example, the collections module 120identifies the plurality of content sources, generates the contentrequests based on the content requirements (e.g., looking for contentassociated with the query request 136), and sends the plurality ofcontent requests 126 to the identified plurality of content sources.

Having issued the plurality of content requests 126, the collectionsmodule 120 interprets a plurality of content responses 128 to determinewhether a response quality level is favorable. The interpreting includesanalyzing the plurality of content responses 128 to produce an estimatedresponse quality level and indicating a favorable response quality levelwhen the estimated response quality level compares favorably to aminimum response quality threshold level (e.g., enough content has beenaccumulated to reliably produce new knowledge that is relevant to thedomain). When the response quality level is favorable, the collectionsmodule 120 issues a collections response 134 to the IEI module 122,where the collections response 134 includes further content. Forexample, the collections module 120 generates the collections response134 to include the further content and the estimated response qualitylevel and sends the collections response 134 to the IEI module 122.

The IEI module 122 analyzes the further content based on one or more ofthe IEI request 244 and the graphically formatted fact base information600 utilizing the rules 316 produce one or more of updated fact baseinformation (e.g., new knowledge for storage in the graphical format inthe graphical database 934 of the SS memory 96) and a preliminary answerwith an associated preliminary answer quality level. For example, theIEI module 122 reasons the further content with the graphicallyformatted fact base information 600 to produce the preliminary answerwhich includes identification of a favorable interpretation of a portionof the query request 136.

When the answer quality level is favorable, the IEI module 122 issues anIEI response 246 to the query module 124 where the IEI response 246includes the preliminary answer associated with a favorable answerquality level. The query module 124 interprets the received answer toproduce a quality level of the received answer. For example, the querymodule 124 analyzes the preliminary answer in accordance with the queryrequirements and the rules to generate the quality level of the receivedanswer. When the quality level of the received answer is favorable, thequery module 124 issues a query response 140 to the user device 12-1,where the query response 140 includes the answer (e.g., the most likelyanswer based on the graphical database information) associated with thefavorable quality level of the answer.

FIG. 16B is a logic diagram of an embodiment of a method for utilizingmultiple database formats when creating knowledge from content within acomputing system. In particular, a method is presented for use inconjunction with one or more functions and features described inconjunction with FIGS. 1-8L, 16A and also FIG. 16B. The method includesstep 940 where a processing module of one or more processing modules ofone or more computing devices of the computing system interprets areceived query request from a requester to produce query requirementswith regards to creating knowledge from trusted content sources. Theinterpreting includes one or more of determining content requirements,(e.g., to identify trust content sources) determining sourcerequirements, determining answer timing requirements, and identifying adomain associated with the query request.

The method continues at step 942 where the processing module IEIprocesses human expressions of the received query request based on afact base generated from previous content and stored in a graphicaldatabase format to produce a preliminary answer, where the processingutilizes rules stored in a single type database format. The processingmay include formatting portions of the query request in accordance withformatting rules stored in the single type database format to producerecognizable human expressions of content and question information. Forexample, the processing module produces the question information toinclude a request to generate new knowledge around a domain.

The processing may further include identifying permutations ofidentigens within the human expressions, reducing the permutations,mapping the reduced permutations to entigens to produce knowledge,representing the knowledge in a graphical database format, processingthe graphical database represented knowledge in accordance with a factbase to produce the preliminary answer, and generating an answer qualitylevel associated with the preliminary answer. For instance, theprocessing module generates a relatively low answer quality level whenthe question relates to gathering information over a subsequent timeframe such that more content must be gathered to produce an answerassociated with a higher and more favorable answer quality level (e.g.,start looking for content associated with the domain of the queryrequest over the next 10 minutes).

When the answer quality level is unfavorable, the method continues atstep 944 where the processing module generates content requirements andobtains further content from a plurality of trusted content sourcesbased on the query requirements. The generating of the contentrequirements includes determining, based on one or more of the queryrequirements, preliminary answer, and the answer quality level, one ormore of content selection requirements, source selection requirements,and acquisition timing requirements.

The obtaining the further content from a plurality of trusted contentsources is based on the content requirements. For example, theprocessing module identifies the plurality of trusted content sources,generates content requests based on the content requirements, and sendsthe plurality of content requests to the plurality of identified trustedcontent sources. The processing module further analyzes a plurality ofcontent responses to produce an estimated quality level, indicatesfavorable quality level when the estimated quality level comparesfavorably to a minimum quality threshold level (e.g., enough content hasbeen collected to produce new knowledge with regards to the domain ofthe query request), and indicates unfavorable quality level tofacilitate collecting more content when the estimated quality levelcompares unfavorably to the minimum quality threshold level.

The method continues at step 946 where the processing module IEIprocesses human expressions of the further content based on the factbase to produce additional knowledge, for the producing utilizes therules stored in the single type database format and produces theadditional knowledge in the graphical database format. For example, theprocessing module analyzes, based on one or more of the query request,the fact base info associated with the identified domain, and thefurther content to produce updated fact base info (e.g., new knowledge).The analyzing may include reasoning the further content with the factbase to produce the updated fact base info.

The method continues at step 948 where the processing module facilitatesstorage of the additional knowledge in a graphical database. The storingincludes one or more of analyzing the new knowledge in accordance withthe query requirements and the rules to generate a quality level,representing the additional knowledge in the graphical database format,and sending the additional knowledge to the graphical database forstorage for storage.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 17A is a schematic block diagram of another embodiment of a contentingestion module 300 within a computing system that includes a charactertransformation module 960 and a repositioning module 962. The charactertransformation module 960 and the repositioning module 962 may beimplemented utilizing the processing module 50-1 of FIG. 2 and/or ofFIG. 3. Generally, an embodiment of this invention presents solutionswhere the content ingestion module 300 processes source content 310and/or question content 312 (e.g., collectively content) for ingestionbased when creating knowledge from the content, where the processing isin accordance with one or more of lists 330 and rules 316 to produceformatted content 314.

In an example of operation of the processing of the content foringestion, the character transformation module 960 obtains a phrase ofwords for analysis from the content. For example, the charactertransformation module 960 receives source content 310 and/or receivedquestion content 312 and extracts words from the received content.

Having obtained the phrase of words, for each word, the charactertransformation module 960 identifies characters of the word that are tobe transformed in accordance with the rules 316 (e.g., characterdeletion rules, character substitution rules, and the lists 330. Forexample, the character transformation module 960 identifies from listedcharacters in the lists 330 for deletion in accordance with a deletionrule of the rules 316. As another example, the character transformationmodule 960 identifies from further listed characters of the lists 330for replacements in accordance with a replacement rule of the rules 316.As yet another example, the character transformation module 960identifies listed characters of the lists 330 to be substituted inaccordance with a substitution rule of the rules 316.

Having identified the characters of each word that are to betransformed, the character transformation module 960 transforms theidentified characters in accordance with the rules 316 utilizing thelist 330 to produce transformed characters 966. For example, thecharacter transformation module 960 deletes unnecessary symbols andpunctuation when the transformation processing includes deletion ofcharacters. As another example, the character transformation module 960eliminates unnecessary sentence starters when the transformationprocessing includes deletion of words (e.g., delete sentence starter“well”). As yet another example, the character transformation module 960replaces contractions with the expanded contractions when thetransformation processing includes word replacements. As a furtherexample, the character transformation module 960 substitutes characterswith other characters when the transformation processing includescharacter substitution (e.g., replaces “-” with “,”).

The repositioning module 962 processes the transformed characters 966 inaccordance with the rules 316 (e.g., further including word orderingrules) to produce the formatted content 314. The processing includesrepositioning words including one or more of moving words and/orcharacters to another position relative to a received ordering tofurther enhance downstream knowledge extraction in accordance with therules 316.

FIG. 17B is a logic diagram of an embodiment of a method for ingestingcontent when creating knowledge from content within a computing system.In particular, a method is presented for use in conjunction with one ormore functions and features described in conjunction with FIGS. 1-8L,17A, and also FIG. 17B. The method includes step 970 where a processingmodule of one or more processing modules of one or more computingdevices of the computing system obtains a phrase of words for ingestionoptimization, where knowledge is to be subsequently extracted from anoptimized form of the phrase. The obtaining includes one or more ofreceiving source content, receiving question content, and extractingwords from the received content.

For each word, the method continues at step 972 where the processingmodule identifies characters of the word that are to be transformed inaccordance with character transformation rules. The identifying includesone or more of finding listed characters for deletion in accordance witha deletion rule, finding listed characters for replacement in accordancewith a replacement rule, and finding listed characters to be substitutedin accordance with a substitution rule.

The method continues at step 974 where the processing module transformedthe identified characters in accordance with the charactertransformation rules to produce transformed characters. For example, theprocessing module deletes unnecessary symbols and punctuation whendeleting characters. As another example, the processing moduleeliminates unnecessary sentence starters.

The method continues at step 976 where the processing module processesthe transformed characters in accordance with repositioning rules toproduce formatted content. For example, the processing modulerepositions words including one or more of moving words and charactersto another position relative to a received ordering to further enhancedownstream knowledge extraction in accordance with the rules.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 18A is a schematic block diagram of another embodiment of aninterpretation module 304 within a computing system that includes thegrouping matching module 404 of FIG. 6A and the grouping interpretationmodule 406 of FIG. 6A. The grouping matching module 404 and the groupinginterpretation module 406 may be implemented utilizing the processingmodule 50-1 of FIG. 2 and/or of FIG. 3. Generally an embodiment of thisinvention presents solutions where the interpretation module 304interprets identified element information 340 (e.g., ingested content)in accordance with groupings list 334, question information 346, andinterpretation rules 320 to produce interpreted information 344 whenknowledge is to be subsequently created from the identified elementinformation 340.

In an example of operation of the interpreting of the content, thegrouping matching module 404 receives the identified element info 340that includes a group of possible interpretations of an ingested phrase,where each interpretation is associated with favorable structure andmatched elements of the phrase in accordance with element rules. Foreach possible interpretation, the grouping matching module 404determines a statistical likelihood of relevance value based ongroupings list 334 and outputs the interpretation with the relevancevalue as rated interpretations info 980 to the grouping interpretationmodule 406. For example, the grouping matching module 404 represents theinterpretation utilizing a graphical database format, correlates thegraphical database format to a previously matched phrase and associatedgraphical database format to produce a score, and output the score asthe statistical likelihood of relevance value.

The grouping interpretation module 406 chooses a selection approach frominterpretation rules 320. The choosing may be based on a variety offactors including one or more of by user, based on history, based onprevious questions, based on knowledge from other knowledge bases, etc.For each interpretation of the rated interpretations info 980, thegrouping interpretation module 406 updates the statistical likelihood ofrelevance value based on question info 346 utilizing the chosenselection approach to associate an updated statistical likelihood ofrelevance value with the interpretation.

The grouping interpretation module 406 selects an interpretation of thegroup of possible interpretations that is associated with a mostfavorable statistical value (e.g., highest, most likely to be true) andindicates a true meaning of the ingested phrase associated with theidentified alternative interpretation as the interpreted information344. The interpreted information 344 may further include otherinterpretations of the group of possible interpretations and for eachpossible interpretation, a corresponding updated statistical value(e.g., ranking).

FIG. 18B is a logic diagram of an embodiment of a method forinterpreting content when creating knowledge from content within acomputing system. In particular, a method is presented for use inconjunction with one or more functions and features described inconjunction with FIGS. 1-8L, 18A, and also FIG. 18B. The method includesstep 990 where a processing module of one or more processing modules ofone or more computing devices of the computing system obtains identifiedelement information that includes a group of possible interpretations ofa portion of ingested content, where each interpretation is associatedwith favorable structure and matched elements of the portion of ingestedcontent in accordance with element rules. For example, the processingmodule receives content and applies identigen entigen intelligence (IEI)processing to the content to produce the identified element information.As another example, the processing module receives the identifiedelement information.

For each possible interpretation, the method continues at step 992 wherethe processing module determines a statistical likelihood of relevancevalue to produce rated interpretation information, where the ratedinterpretation information includes the interpretation and thestatistical likelihood of relevance value. For example, the processingmodule represents the interpretation utilizing a graphical databaseformat, correlates the graphical database format to a previously matchedphrase of a knowledge base in a graphical database format to produce ascore, and outputs the score as the statistical likelihood of relevancevalue.

The method continues at step 994 where the processing module chooses aselection approach for subsequent selection of a most relevantinterpretation of the group of possible interpretations. For example,the processing module chooses based on one or more of by user, based onhistory, based on previous questions, based on knowledge from otherknowledge bases, etc.

For each interpretation of the rated interpretation information, themethod continues at step 996 where the processing module updates thestatistical likelihood of relevance value based on the chosen selectionapproach and question context. For example, the processing moduleupdates the graphical database format of the interpretation based on thequestion context, correlates the updated graphical database format to apreviously matched phrase of the knowledge base in the graphicaldatabase format to produce an updated score, and outputs the updatedscore as an updated statistical likelihood of relevance value.

The method continues at step 998 where the processing module selects aninterpretation of the group of possible interpretations that isassociated with a most favorable updated statistical likelihood ofrelevance value to indicating meaning. For example, the processingmodule identifies and interpretation associated with a highest relevancevalue and outputs each interpretation and each associated relevancevalue in an ordered fashion.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by a computing device, themethod comprises: generating a plurality of entigen groups from a set ofphrases of a statement, wherein the plurality of entigen groupsrepresents a plurality of most likely meanings for the set of phrases;identifying two plausible entigen groups of the plurality of entigengroups based on a true meaning interpretation of the statement, whereinthe two plausible entigen groups are substantially equally likelyinterpretations of the statement based on an ambiguity in the statement;identifying a related entigen group based on a phrase of the statement;interpreting each of the two plausible entigen groups in light of therelated entigen group to determine whether one of the two plausibleentigen groups is a more likely interpretation of the statement than theother one of the two plausible entigen groups; when the one of the twoplausible entigen groups is the more likely interpretation of thestatement, updating the one of the two plausible entigen groups inaccordance with the related entigen group to produce an updated entigengroup; and adding the statement as the updated entigen group to aknowledge database.
 2. The method of claim 1 further comprises:obtaining a query entigen group; and generating a query response to thequery entigen group utilizing the updated entigen group.
 3. The methodof claim 1 further comprises: identifying a second related entigen groupbased a second phrase of the statement; interpreting each of the twoplausible entigen groups in light of the related entigen group and thesecond related entigen group to determine whether the one of the twoplausible entigen groups is the more likely interpretation of thestatement than the other one of the two plausible entigen groups; andwhen the one of the two plausible entigen groups is the more likelyinterpretation of the statement, updating the one of the two plausibleentigen groups in accordance with the related entigen group and thesecond related entigen group to produce the updated entigen group. 4.The method of claim 1 further comprises: applying the related entigengroup to a first plausible entigen group of the two plausible entigengroups to create a first augmented entigen group of two augmentedentigen groups; applying the related entigen group to a second plausibleentigen group of the two plausible entigen groups to create a secondaugmented entigen group of the two augmented entigen groups; comparingthe first augmented entigen group to the second augmented entigen groupto determine whether one of the two augmented entigen groups is a morelikely interpretation of the statement than the other one of the twoaugmented entigen groups; and when the one of the two augmented entigengroups is the more likely interpretation of the statement, generatingthe updated entigen group based on the one of the two augmented entigengroups.
 5. The method of claim 1, wherein the identifying the twoplausible entigen groups of the plurality of entigen groups based on thetrue meaning interpretation of the statement comprises: interpreting,based on language rules, a plurality of sets of identigens to produce afirst plausible entigen group of the two plausible entigen groups,wherein the first plausible entigen group represents a first truemeaning interpretation of the set of phrases of the statement, wherein aset of identigens of the plurality of sets of identigens includes one ormore different meanings of a word of the set of phrases, wherein anentigen of the first plausible entigen group corresponds to an identigenof the set identigens having a selected meaning of the one or moredifferent meanings of the word; and interpreting, based on the languagerules, the plurality of sets of identigens to produce a second plausibleentigen group of the two plausible entigen groups, wherein the secondplausible entigen group represents a second true meaning interpretationof the set of phrases of the statement, wherein an entigen of the secondplausible entigen group corresponds to another identigen of the setidentigens having another selected meaning of the one or more differentmeanings of the word.
 6. The method of claim 1, wherein the identifyingthe related entigen group based on the phrase of the statementcomprises: selecting the phrase of the set of phrases based on theambiguity in the statement; identifying one or more related entigengroup attributes based on the phrase; and obtaining the related entigengroup by at least one of: accessing the knowledge database based on theone or more related entigen group attributes to obtain the relatedentigen group; and accessing a content source to obtain content based onthe one or more related entigen group attributes to enable subsequentgeneration of the related entigen group.
 7. A computing device of acomputing system, the computing device comprises: an interface; a localmemory; and a processing module operably coupled to the interface andthe local memory, wherein the processing module functions to: generate aplurality of entigen groups from a set of phrases of a statement,wherein the plurality of entigen groups represents a plurality of mostlikely meanings for the set of phrases; identify two plausible entigengroups of the plurality of entigen groups based on a true meaninginterpretation of the statement, wherein the two plausible entigengroups are substantially equally likely interpretations of the statementbased on an ambiguity in the statement; identify a related entigen groupbased a phrase of the statement; interpret each of the two plausibleentigen groups in light of the related entigen group to determinewhether one of the two plausible entigen groups is a more likelyinterpretation of the statement than the other one of the two plausibleentigen groups; when the one of the two plausible entigen groups is themore likely interpretation of the statement, update the one of the twoplausible entigen groups in accordance with the related entigen group toproduce an updated entigen group; and add, via the interface, thestatement as the updated entigen group to a knowledge database.
 8. Thecomputing device of claim 7, wherein the processing module furtherfunctions to: obtain a query entigen group; and generate a queryresponse to the query entigen group utilizing the updated entigen group.9. The computing device of claim 7, wherein the processing modulefurther functions to: identify a second related entigen group based asecond phrase of the statement; interpret each of the two plausibleentigen groups in light of the related entigen group and the secondrelated entigen group to determine whether the one of the two plausibleentigen groups is the more likely interpretation of the statement thanthe other one of the two plausible entigen groups; and when the one ofthe two plausible entigen groups is the more likely interpretation ofthe statement, update the one of the two plausible entigen groups inaccordance with the related entigen group and the second related entigengroup to produce the updated entigen group.
 10. The computing device ofclaim 7, wherein the processing module further functions to: apply therelated entigen group to a first plausible entigen group of the twoplausible entigen groups to create a first augmented entigen group oftwo augmented entigen groups; apply the related entigen group to asecond plausible entigen group of the two plausible entigen groups tocreate a second augmented entigen group of the two augmented entigengroups; compare the first augmented entigen group to the secondaugmented entigen group to determine whether one of the two augmentedentigen groups is a more likely interpretation of the statement than theother one of the two augmented entigen groups; and when the one of thetwo augmented entigen groups is the more likely interpretation of thestatement, generate the updated entigen group based on the one of thetwo augmented entigen groups.
 11. The computing device of claim 7,wherein the processing module functions to identify the two plausibleentigen groups of the plurality of entigen groups based on the truemeaning interpretation of the statement by: interpreting, based onlanguage rules, a plurality of sets of identigens to produce a firstplausible entigen group of the two plausible entigen groups, wherein thefirst plausible entigen group represents a first true meaninginterpretation of the set of phrases of the statement, wherein a set ofidentigens of the plurality of sets of identigens includes one or moredifferent meanings of a word of the set of phrases, wherein an entigenof the first plausible entigen group corresponds to an identigen of theset identigens having a selected meaning of the one or more differentmeanings of the word; and interpreting, based on the language rules, theplurality of sets of identigens to produce a second plausible entigengroup of the two plausible entigen groups, wherein the second plausibleentigen group represents a second true meaning interpretation of the setof phrases of the statement, wherein an entigen of the second plausibleentigen group corresponds to another identigen of the set identigenshaving another selected meaning of the one or more different meanings ofthe word.
 12. The computing device of claim 7, wherein the processingmodule functions to identify the related entigen group based on thephrase of the statement by: selecting the phrase of the set of phrasesbased on the ambiguity in the statement; identifying one or more relatedentigen group attributes based on the phrase; and obtaining the relatedentigen group by at least one of: accessing, via the interface, theknowledge database based on the one or more related entigen groupattributes to obtain the related entigen group; and accessing, via theinterface, a content source to obtain content based on the one or morerelated entigen group attributes to enable subsequent generation of therelated entigen group.
 13. A computer readable memory comprises: a firstmemory element that stores operational instructions that, when executedby a processing module, causes the processing module to: generate aplurality of entigen groups from a set of phrases of a statement,wherein the plurality of entigen groups represents a plurality of mostlikely meanings for the set of phrases; a second memory element thatstores operational instructions that, when executed by the processingmodule, causes the processing module to: identify two plausible entigengroups of the plurality of entigen groups based on a true meaninginterpretation of the statement, wherein the two plausible entigengroups are substantially equally likely interpretations of the statementbased on an ambiguity in the statement; a third memory element thatstores operational instructions that, when executed by the processingmodule, causes the processing module to: identify a related entigengroup based a phrase of the statement; a fourth memory element thatstores operational instructions that, when executed by the processingmodule, causes the processing module to: interpret each of the twoplausible entigen groups in light of the related entigen group todetermine whether one of the two plausible entigen groups is a morelikely interpretation of the statement than the other one of the twoplausible entigen groups; a fifth memory element that stores operationalinstructions that, when executed by the processing module, causes theprocessing module to: when the one of the two plausible entigen groupsis the more likely interpretation of the statement, update the one ofthe two plausible entigen groups in accordance with the related entigengroup to produce an updated entigen group; and a sixth memory elementthat stores operational instructions that, when executed by theprocessing module, causes the processing module to: add the statement asthe updated entigen group to a knowledge database.
 14. The computerreadable memory of claim 13 further comprises: a seventh memory elementthat stores operational instructions that, when executed by theprocessing module, causes the processing module to: obtain a queryentigen group; and generate a query response to the query entigen grouputilizing the updated entigen group.
 15. The computer readable memory ofclaim 13 further comprises: the third memory element further storesoperational instructions that, when executed by the processing module,causes the processing module to: identify a second related entigen groupbased a second phrase of the statement; the fourth memory elementfurther stores operational instructions that, when executed by theprocessing module, causes the processing module to: interpret each ofthe two plausible entigen groups in light of the related entigen groupand the second related entigen group to determine whether the one of thetwo plausible entigen groups is the more likely interpretation of thestatement than the other one of the two plausible entigen groups; andthe fifth memory element further stores operational instructions that,when executed by the processing module, causes the processing module to:when the one of the two plausible entigen groups is the more likelyinterpretation of the statement, update the one of the two plausibleentigen groups in accordance with the related entigen group and thesecond related entigen group to produce the updated entigen group. 16.The computer readable memory of claim 13 further comprises: a seventhmemory element that stores operational instructions that, when executedby the processing module, causes the processing module to: apply therelated entigen group to a first plausible entigen group of the twoplausible entigen groups to create a first augmented entigen group oftwo augmented entigen groups; apply the related entigen group to asecond plausible entigen group of the two plausible entigen groups tocreate a second augmented entigen group of the two augmented entigengroups; compare the first augmented entigen group to the secondaugmented entigen group to determine whether one of the two augmentedentigen groups is a more likely interpretation of the statement than theother one of the two augmented entigen groups; and when the one of thetwo augmented entigen groups is the more likely interpretation of thestatement, generate the updated entigen group based on the one of thetwo augmented entigen groups.
 17. The computer readable memory of claim13, wherein the processing module functions to execute the operationalinstructions stored by the second memory element to cause the processingmodule to identify the two plausible entigen groups of the plurality ofentigen groups based on the true meaning interpretation of the statementby: interpreting, based on language rules, a plurality of sets ofidentigens to produce a first plausible entigen group of the twoplausible entigen groups, wherein the first plausible entigen grouprepresents a first true meaning interpretation of the set of phrases ofthe statement, wherein a set of identigens of the plurality of sets ofidentigens includes one or more different meanings of a word of the setof phrases, wherein an entigen of the first plausible entigen groupcorresponds to an identigen of the set identigens having a selectedmeaning of the one or more different meanings of the word; andinterpreting, based on the language rules, the plurality of sets ofidentigens to produce a second plausible entigen group of the twoplausible entigen groups, wherein the second plausible entigen grouprepresents a second true meaning interpretation of the set of phrases ofthe statement, wherein an entigen of the second plausible entigen groupcorresponds to another identigen of the set identigens having anotherselected meaning of the one or more different meanings of the word. 18.The computer readable memory of claim 13, wherein the processing modulefunctions to execute the operational instructions stored by the thirdmemory element to cause the processing module to identify the relatedentigen group based on the phrase of the statement by: selecting thephrase of the set of phrases based on the ambiguity in the statement;identifying one or more related entigen group attributes based on thephrase; and obtaining the related entigen group by at least one of:accessing the knowledge database based on the one or more relatedentigen group attributes to obtain the related entigen group; andaccessing a content source to obtain content based on the one or morerelated entigen group attributes to enable subsequent generation of therelated entigen group.